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Current challenges and visions in music recommender systems research

• Trends and Surveys
• Open access
• Published: 05 April 2018
• Volume 7 , pages 95–116, ( 2018 )

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• Markus Schedl   ORCID: orcid.org/0000-0003-1706-3406 1 ,
• Hamed Zamani 2 ,
• Ching-Wei Chen 3 ,
• Yashar Deldjoo 4 &
• Mehdi Elahi 5  

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Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user’s fingertip. While today’s MRSs considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user–item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art toward solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.

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1 Introduction

Research in music recommender systems (MRSs) has recently experienced a substantial gain in interest both in academia and in industry [ 162 ]. Thanks to music streaming services like Spotify, Pandora, or Apple Music, music aficionados are nowadays given access to tens of millions music pieces. By filtering this abundance of music items, thereby limiting choice overload [ 20 ], MRSs are often very successful to suggest songs that fit their users’ preferences. However, such systems are still far from being perfect and frequently produce unsatisfactory recommendations. This is partly because of the fact that users’ tastes and musical needs are highly dependent on a multitude of factors, which are not considered in sufficient depth in current MRS approaches, which are typically centered on the core concept of user–item interactions, or sometimes content-based item descriptors. In contrast, we argue that satisfying the users’ musical entertainment needs requires taking into account intrinsic, extrinsic, and contextual aspects of the listeners [ 2 ], as well as more decent interaction information. For instance, personality and emotional state of the listeners (intrinsic) [ 71 , 147 ] as well as their activity (extrinsic) [ 75 , 184 ] are known to influence musical tastes and needs. So are users’ contextual factors including weather conditions, social surrounding, or places of interest [ 2 , 100 ]. Also the composition and annotation of a music playlist or a listening session reveal information about which songs go well together or are suited for a certain occasion [ 126 , 194 ]. Therefore, researchers and designers of MRS should reconsider their users in a holistic way in order to build systems tailored to the specificities of each user.

Against this background, in this trends and survey article, we elaborate on what we believe to be among the most pressing current challenges in MRS research, by discussing the respective state of the art and its restrictions (Sect.  2 ). Not being able to touch all challenges exhaustively, we focus on cold start , automatic playlist continuation , and evaluation of MRS. While these problems are to some extent prevalent in other recommendation domains too, certain characteristics of music pose particular challenges in these contexts. Among them are the short duration of items (compared to movies), the high emotional connotation of music, and the acceptance of users for duplicate recommendations. In the second part, we present our visions for future directions in MRS research (Sect.  3 ). More precisely, we elaborate on the topics of psychologically inspired music recommendation (considering human personality and emotion), situation-aware music recommendation , and culture-aware music recommendation . We conclude this article with a summary and identification of possible starting points for the interested researcher to face the discussed challenges (Sect.  4 ).

The composition of the authors allows to take academic as well as industrial perspectives, which are both reflected in this article. Furthermore, we would like to highlight that particularly the ideas presented as Challenge 2: Automatic playlist continuation in Sect.  2 play an important role in the task definition, organization, and execution of the ACM Recommender Systems Challenge 2018 Footnote 1 which focuses on this use case. This article may therefore also serve as an entry point for potential participants in this challenge.

2 Grand challenges

In the following, we identify and detail a selection of the grand challenges, which we believe the research field of music recommender systems is currently facing, i.e., overcoming the cold start problem, automatic playlist continuation, and properly evaluating music recommender systems. We review the state of the art of the respective tasks and its current limitations.

2.1 Particularities of music recommendation

Before we start digging deeper into these challenges, we would first like to highlight the major aspects that make music recommendation a particular endeavor and distinguishes it from recommending other items, such as movies, books, or products. These aspects have been adapted and extended from a tutorial on music recommender systems [ 161 ], co-presented by one of the authors at the ACM Recommender Systems 2017 conference. Footnote 2

Duration of items In traditional movie recommendation, the items of interest have a typical duration of 90 min or more. In book recommendation, the consumption time is commonly even much longer. In contrast, the duration of music items usually ranges between 3 and 5 min (except maybe for classical music). Because of this, music items may be considered more disposable.

Magnitude of items The size of common commercial music catalogs is in the range of tens of millions music pieces, while movie streaming services have to deal with much smaller catalog sizes, typically thousands up to tens of thousands of movies and series. Footnote 3 Scalability is therefore a much more important issue in music recommendation than in movie recommendation.

Sequential consumption Unlike movies, music pieces are most frequently consumed sequentially, more than one at a time, i.e., in a listening session or playlist. This yields a number of challenges for a MRS, which relate to identifying the right arrangement of items in a recommendation list.

Recommendation of previously recommended items Recommending the same music piece again, at a later point in time, may be appreciated by the user of a MRS, in contrast to a movie or product recommender, where repeated recommendations are usually not preferred.

Consumption behavior Music is often consumed passively, in the background. While this is not a problem per se, it can affect preference elicitation. In particular when using implicit feedback to infer listener preferences, the fact that a listener is not paying attention to the music (therefore, e.g., not skipping a song) might be wrongly interpreted as a positive signal.

Listening intent and purpose Music serves various purposes for people and hence shapes their intent to listen to it. This should be taken into account when building a MRS. In extensive literature and empirical studies, Schäfer et al. [ 155 ] distilled three fundamental intents of music listening out of 129 distinct music uses and functions: self-awareness, social relatedness, and arousal and mood regulation. Self-awareness is considered as a very private relationship with music listening. The self-awareness dimension “helps people think about who they are, who they would like to be, and how to cut their own path” [ 154 ]. Social relatedness  [ 153 ] describes the use of music to feel close to friends and to express identity and values to others. Mood regulation is concerned with managing emotions, which is a critical issue when it comes to the well-being of humans [ 77 , 110 , 176 ]. In fact, several studies found that mood and emotion regulation is the most important purpose why people listen to music [ 18 , 96 , 122 , 155 ], for which reason we discuss the particular role emotions play when listening to music separately below.

Emotions Music is known to evoke very strong emotions. Footnote 4 This is a mutual relationship, though, since also the emotions of users affect musical preferences [ 17 , 77 , 144 ]. Due to this strong relationship between music and emotions, the problem of automatically describing music in terms of emotion words is an active research area, commonly refereed to as music emotion recognition (MER), e.g., [ 14 , 103 , 187 ]. Even though MER can be used to tag music by emotion terms, how to integrate this information into MRS is a highly complicated task, for three reasons. First, MER approaches commonly neglect the distinction between intended emotion (i.e., the emotion the composer, songwriter, or performer had in mind when creating or performing the piece), perceived emotion (i.e., the emotion recognized while listening), and induced emotion that is felt by the listener. Second, the preference for a certain kind of emotionally laden music piece depends on whether the user wants to enhance or to modulate her mood. Third, emotional changes often occur within the same music piece, whereas tags are commonly extracted for the whole piece. Matching music and listeners in terms of emotions therefore requires to model the listener’s musical preference as a time-dependent function of their emotional experiences, also considering the intended purpose (mood enhancement or regulation). This is a highly challenging task and usually neglected in current MRS, for which reason we discuss emotion-aware MRS as one of the main future directions in MRS research, cf. Sect.  3.1 .

Listening context Situational or contextual aspects [ 15 , 48 ] have a strong influence on music preference, consumption, and interaction behavior. For instance, a listener will likely create a different playlist when preparing for a romantic dinner than when warming-up with friends to go out on a Friday night [ 75 ]. The most frequently considered types of context include location (e.g., listening at workplace, when commuting, or relaxing at home) [ 100 ] and time (typically categorized into, for example, morning, afternoon, and evening) [ 31 ]. Context may, in addition, also relate to the listener’s activity [ 184 ], weather [ 140 ], or the use of different listening devices, e.g., earplugs on a smartphone vs. hi-fi stereo at home [ 75 ], to name a few. Since music listening is also a highly social activity, investigating the social context of the listeners is crucial to understand their listening preferences and behavior [ 45 , 134 ]. The importance of considering such contextual factors in MRS research is acknowledged by discussing situation-aware MRS as a trending research direction, cf. Sect.  3.2 .

2.2 Challenge 1: Cold start problem

Problem definition   One of the major problems of recommender systems in general [ 64 , 151 ], and music recommender systems in particular [ 99 , 119 ] is the cold start problem, i.e., when a new user registers to the system or a new item is added to the catalog and the system does not have sufficient data associated with these items/users. In such a case, the system cannot properly recommend existing items to a new user ( new user problem) or recommend a new item to the existing users ( new item problem) [ 3 , 62 , 99 , 164 ].

Another subproblem of cold start is the sparsity problem which refers to the fact that the number of given ratings is much lower than the number of possible ratings, which is particularly likely when the number of users and items is large. The inverse of the ratio between given and possible ratings is called sparsity. High sparsity translates into low rating coverage , since most users tend to rate only a tiny fraction of items. The effect is that recommendations often become unreliable [ 99 ]. Typical values of sparsity are quite close to 100% in most real-world recommender systems. In the music domain, this is a particularly substantial problem. Dror et al. [ 51 ], for instance, analyzed the Yahoo! Music dataset, which as of time of writing represents the largest music recommendation dataset. They report a sparsity of 99.96%. For comparison, the Netflix dataset of movies has a sparsity of “only” 98.82%. Footnote 5

State of the art

A number of approaches have already been proposed to tackle the cold start problem in the music recommendation domain, foremost content-based approaches, hybridization, cross-domain recommendation, and active learning.

Content-based recommendation (CB) algorithms do not require ratings of users other than the target user. Therefore, as long as some pieces of information about the user’s own preferences are available, such techniques can be used in cold start scenarios. Furthermore, in the most severe case, when a new item is added to the catalog, content-based methods enable recommendations, because they can extract features from the new item and use them to make recommendations. It is noteworthy that while collaborative filtering (CF) systems have cold start problems both for new users and new items, content-based systems have only cold start problems for new users [ 5 ].

As for the new item problem, a standard approach is to extract a number of features that define the acoustic properties of the audio signal and use content-based learning of the user interest (user profile learning) in order to effect recommendations. Feature extraction is typically done automatically, but can also be effected manually by musical experts, as in the case of Pandora’s Music Genome Project. Footnote 6 Pandora uses up to 450 specific descriptors per song, such as “aggressive female vocalist,” “prominent backup vocals,” “abstract lyrics,” or “use of unusual harmonies.” Footnote 7 Regardless of whether the feature extraction process is performed automatically or manually, this approach is advantageous not only to address the new item problem but also because an accurate feature representation can be highly predicative of users’ tastes and interests which can be leveraged in the subsequent information filtering stage [ 5 ]. An advantage of music to video is that features in music are limited to a single audio channel, compared to audio and visual channels for videos adding a level complexity to the content analysis of videos explored individually or multimodal in different research works [ 46 , 47 , 59 , 128 ].

Automatic feature extraction from audio signals can be done in two main manners: (1) by extracting a feature vector from each item individually, independent of other items, or (2) by considering the cross-relation between items in the training dataset. The difference is that in (1) the same process is performed in the training and testing phases of the system, and the extracted feature vectors can be used off-the-shelf in the subsequent processing stage; for example, they can be used to compute similarities between items in a one-to-one fashion at testing time. In contrast, in (2) first a model is built from all features extracted in the training phase, whose main role is to map the features into a new (acoustic) space in which the similarities between items are better represented and exploited. An example of approach (1) is the block-level feature framework [ 167 , 168 ], which creates a feature vector of about 10,000 dimensions, independently for each song in the given music collection. This vector describes aspects such as spectral patterns, recurring beats, and correlations between frequency bands. An example of strategy (2) is to create a low-dimensional i-vector representation from the Mel-frequency cepstral coefficients (MFCCs), which model musical timbre to some extent [ 56 ]. To this end, a universal background model is created from the MFCC vectors of the whole music collection, using a Gaussian mixture model (GMM). Performing factor analysis on a representation of the GMM eventually yields i-vectors.

In scenarios where some form of semantic labels, e.g., genres or musical instruments, are available, it is possible to build models that learn the intermediate mapping between low-level audio features and semantic representations using machine learning techniques, and subsequently use the learned models for prediction. A good point of reference for such semantic-inferred approaches can be found in [ 19 , 36 ].

An alternative technique to tackle the new item problem is hybridization . A review of different hybrid and ensemble recommender systems can be found in [ 6 , 26 ]. In [ 50 ], the authors propose a music recommender system which combines an acoustic CB and an item-based CF recommender. For the content-based component, it computes acoustic features including spectral properties, timbre, rhythm, and pitch. The content-based component then assists the collaborative filtering recommender in tackling the cold start problem since the features of the former are automatically derived via audio content analysis.

The solution proposed in [ 189 ] is a hybrid recommender system that combines CF and acoustic CB strategies also by feature hybridization. However, in this work the feature-level hybridization is not performed in the original feature domain. Instead, a set of latent variables referred to as conceptual genre are introduced, whose role is to provide a common shared feature space for the two recommenders and enable hybridization. The weights associated with the latent variables reflect the musical taste of the target user and are learned during the training stage.

In [ 169 ], the authors propose a hybrid recommender system incorporating item–item CF and acoustic CB based on similarity metric learning. The proposed metric learning is an optimization model that aims to learn the weights associated with the audio content features (when combined in a linear fashion) so that a degree of consistency between CF-based similarity and the acoustic CB similarity measure is established. The optimization problem can be solved using quadratic programming techniques.

Another solution to cold start is cross-domain recommendation techniques, which aim at improving recommendations in one domain (here music) by making use of information about the user preferences in an auxiliary domain [ 28 , 67 ]. Hence, the knowledge of the preferences of the user is transferred from an auxiliary domain to the music domain, resulting in a more complete and accurate user model. Similarly, it is also possible to integrate additional pieces of information about the (new) users, which are not directly related to music, such as their personality, in order to improve the estimation of the user’s music preferences. Several studies conducted on user personality characteristics support the conjecture that it may be useful to exploit this information in music recommender systems [ 69 , 73 , 86 , 130 , 147 ]. For a more detailed literature review of cross-domain recommendation, we refer to [ 29 , 68 , 102 ].

In addition to the aforementioned approaches, active learning has shown promising results in dealing with the cold start problem in single domain [ 60 , 146 ] or cross-domain recommendation scenario [ 136 , 192 ]. Active learning addresses this problem at its origin by identifying and eliciting (high quality) data that can represent the preferences of users better than by what they provide themselves. Such a system therefore interactively demands specific user feedback to maximize the improvement of system performance.

Limitations   The state-of-the-art approaches elaborated on above are restricted by certain limitations. When using content-based filtering , for instance, almost all existing approaches rely on a number of predefined audio features that have been used over and over again, including spectral features, MFCCs, and a great number of derivatives [ 106 ]. However, doing so assumes that (all) these features are predictive of the user’s music taste, while in practice it has been shown that the acoustic properties that are important for the perception of music are highly subjective [ 132 ]. Furthermore, listeners’ different tastes and levels of interest in different pieces of music influence perception of item similarity [ 158 ]. This subjectiveness demands for CB recommenders that incorporate personalization in their mathematical model. For example, in [ 65 ] the authors propose a hybrid (CB+CF) recommender model, namely regression-based latent factor models (RLFM). In [ 4 ], the authors propose a user-specific feature-based similarity model (UFSM), which defines a similarity function for each user, leading to a high degree of personalization. Although not designed specifically for the music domain, the authors of [ 4 ] provide an interesting literature review of similar user-specific models.

While hybridization can therefore alleviate the cold start problem to a certain extent, as seen in the examples above, respective approaches are often complex, computationally expensive, and lack transparency [ 27 ]. In particular, results of hybrids employing latent factor models are typically hard to understand for humans.

A major problem with cross-domain recommender systems is their need for data that connects two or more target domains, e.g., books, movies, and music [ 29 ]. In order for such approaches to work properly, items, users, or both therefore need to overlap to a certain degree [ 40 ]. In the absence of such overlap, relationships between the domains must be established otherwise, e.g., by inferring semantic relationships between items in different domains or assuming similar rating patterns of users in the involved domains. However, whether respective approaches are capable of transferring knowledge between domains is disputed [ 39 ]. A related issue in cross-domain recommendation is that there is a lack of established datasets with clear definitions of domains and recommendation scenarios [ 102 ]. Because of this, the majority of existing work on cross-domain RS uses some type of conventional recommendation dataset transformation to suit it for their need.

Finally, also active learning techniques suffer from a number of issues. First of all, the typical active learning techniques propose to a user to rate the items that the system has predicted to be interesting for them, i.e., the items with highest predicted ratings. This indeed is a default strategy in recommender systems for eliciting ratings since users tend to rate what has been recommended to them. Even when users browse the item catalog, they are more likely to rate items which they like or are interested in, rather than those items that they dislike or are indifferent to. Indeed, it has been shown that doing so creates a strong bias in the collected rating data as the database gets populated disproportionately with high ratings. This in turn may substantially influence the prediction algorithm and decrease the recommendation accuracy [ 63 ].

Moreover, not all the active learning strategies are necessarily personalized. The users differ very much in the amount of information they have about the items, their preferences, and the way they make decisions. Hence, it is clearly inefficient to request all the users to rate the same set of items, because many users may have a very limited knowledge, ignore many items, and will therefore not provide ratings for these items. Properly designed active learning techniques should take this into account and propose different items to different users to rate. This can be highly beneficial and increase the chance of acquiring ratings of higher quality [ 57 ].

Moreover, the traditional interaction model designed for active learning in recommender systems can support building the initial profile of a user mainly in the sign-up process. This is done by generating a user profile by requesting the user to rate a set of selected items [ 30 ]. On the other hand, the users must be able to also update their profile by providing more ratings anytime they are willing to. This requires the system to adopt a conversational interaction model [ 30 ], e.g., by exploiting novel interactive design elements in the user interface [ 38 ], such as explanations that can describe the benefits of providing more ratings and motivating the user to do so.

Finally, it is important to note that in an up-and-running recommender system, the ratings are given by users not only when requested by the system (active learning) but also when a user voluntarily explores the item catalog and rates some familiar items (natural acquisition of ratings) [ 30 , 61 , 63 , 127 , 146 ]. While this could have a huge impact on the performance of the system, it has been mostly ignored by the majority of the research works in the field of active learning for recommender systems. Indeed, almost all research works have been based on a rather non-realistic assumption that the only source for collecting new ratings is through the system requests. Therefore, it is crucial to take into account a more realistic scenario when studying the active learning techniques in recommender systems, which can better picture how the system evolves over time when ratings are provided by users [ 143 , 146 ].

2.3 Challenge 2: Automatic playlist continuation

Problem definition   In its most generic definition, a playlist is simply a sequence of tracks intended to be listened to together. The task of automatic playlist generation (APG) then refers to the automated creation of these sequences of tracks. In this context, the ordering of songs in a playlist to generate is often highlighted as a characteristics of APG, which is a highly complex endeavor. Some authors have therefore proposed approaches based on Markov chains to model the transitions between songs in playlists, e.g., [ 32 , 125 ]. While these approaches have been shown to outperform approaches agnostic of the song order in terms of log-likelihood, recent research has found little evidence that the exact order of songs actually matters to users [ 177 ], while the ensemble of songs in a playlist [ 181 ] and direct song-to-song transitions [ 92 ] do matter.

Considered a variation of APG, the task of automatic playlist continuation (APC) consists of adding one or more tracks to a playlist in a way that fits the same target characteristics of the original playlist. This has benefits in both the listening and creation of playlists: users can enjoy listening to continuous sessions beyond the end of a finite-length playlist, while also finding it easier to create longer, more compelling playlists without needing to have extensive musical familiarity.

A large part of the APC task is to accurately infer the intended purpose of a given playlist. This is challenging not only because of the broad range of these intended purposes (when they even exist), but also because of the diversity in the underlying features or characteristics that might be needed to infer those purposes.

Related to Challenge 1, an extreme cold start scenario for this task is where a playlist is created with some metadata (e.g., the title of a playlist), but no song has been added to the playlist. This problem can be cast as an ad hoc information retrieval task , where the task is to rank songs in response to a user-provided metadata query.

The APC task can also potentially benefit from user profiling, e.g., making use of previous playlists and the long-term listening history of the user. We call this personalized playlist continuation .

According to a study carried out in 2016 by the Music Business Association Footnote 8 as part of their Music Biz Consumer Insights program, Footnote 9 playlists accounted for 31% of music listening time among listeners in the USA, more than albums (22%), but less than single tracks (46%). Other studies, conducted by MIDiA, Footnote 10 show that 55% of streaming music service subscribers create music playlists, with some streaming services such as Spotify currently hosting over 2 billion playlists. Footnote 11 In a 2017 study conducted by Nielsen, Footnote 12 it was found that 58% of users in the USA create their own playlists, 32% share them with others. Studies like these suggest a growing importance of playlists as a mode of music consumption, and as such, the study of APG and APC has never been more relevant.

State of the art   APG has been studied ever since digital multimedia transmission made huge catalogs of music available to users. Bonnin and Jannach provide a comprehensive survey of this field in [ 21 ]. In it, the authors frame the APG task as the creation of a sequence of tracks that fulfill some “target characteristics” of a playlist, given some “background knowledge” of the characteristics of the catalog of tracks from which the playlist tracks are drawn. Existing APG systems tackle both of these problems in many different ways.

In early approaches [ 9 , 10 , 135 ] the target characteristics of the playlist are specified as multiple explicit constraints, which include musical attributes or metadata such as artist, tempo, and style. In others, the target characteristics are a single seed track [ 121 ] or a start and an end track [ 9 , 32 , 74 ]. Other approaches create a circular playlist that comprises all tracks in a given music collection, in such a way that consecutive songs are as similar as possible [ 105 , 142 ]. In other works, playlists are created based on the context of the listener, either as single source [ 157 ] or in combination with content-based similarity [ 35 , 149 ].

A common approach to build the background knowledge of the music catalog for playlist generation is using machine learning techniques to extract that knowledge from manually curated playlists. The assumption here is that curators of these playlists are encoding rich latent information about which tracks go together to create a satisfying listening experience for an intended purpose. Some proposed APG and APC systems are trained on playlists from sources such as online radio stations [ 32 , 123 ], online playlist websites [ 126 , 181 ], and music streaming services [ 141 ]. In the study by Pichl et al. [ 141 ], the names of playlists on Spotify were analyzed to create contextual clusters, which were then used to improve recommendations.

An approach to specifically address song ordering within playlists is the use of generative models that are trained on hand-curated playlists. McFee and Lanckriet [ 125 ] represent songs by metadata, familiarity, and audio content features, adopting ideas from statistical natural language processing. They train various Markov chains to model transitions between songs. Similarly, Chen et al. [ 32 ] propose a logistic Markov embedding to model song transitions. This is similar to matrix decomposition methods and results in an embedding of songs in Euclidean space. In contrast to McFee and Lanckriet’s model, Chen et al.’s model does not use any audio features.

Limitations   While some work on automated playlist continuation highlights the special characteristics of playlists, i.e., their sequential order , it is not well understood to which extent and in which cases taking into account the order of tracks in playlists helps create better models for recommendation. For instance, in [ 181 ] Vall et al. recently demonstrated on two datasets of hand-curated playlists that the song order seems to be negligible for accurate playlist continuation when a lot of popular songs are present. On the other hand, the authors argue that order does matter when creating playlists with tracks from the long tail. Another study by McFee and Lanckriet [ 126 ] also suggests that transition effects play an important role in modeling playlist continuity. This is in line with a study presented by Kamehkhosh et al. in [ 92 ], in which users identified song order as being the second but last important criterion for playlist quality. Footnote 13 In another recent user study [ 177 ] conducted by Tintarev et al. the authors found that many participants did not care about the order of tracks in recommended playlists, sometimes they did not even notice that there is a particular order. However, this study was restricted to 20 participants who used the Discover Weekly service of Spotify. Footnote 14

Another challenge for APC is evaluation: in other words, how to assess the quality of a playlist. Evaluation in general is discussed in more detail in the next section, but there are specific questions around evaluation of playlists that should be pointed out here. As Bonnin and Jannach [ 21 ] put it, the ultimate criterion for this is user satisfaction , but that is not easy to measure. In [ 125 ], McFee and Lanckriet categorize the main approaches to APG evaluation as human evaluation, semantic cohesion, and sequence prediction. Human evaluation comes closest to measuring user satisfaction directly, but suffers from problems of scale and reproducibility. Semantic cohesion as a quality metric is easily measurable and reproducible, but assumes that users prefer playlists where tracks are similar along a particular semantic dimension, which may not always be true, see, for instance, the studies carried out by Slaney and White [ 172 ] and by Lee [ 115 ]. Sequence prediction casts APC as an information retrieval task, but in the domain of music, an inaccurate prediction needs not be a bad recommendation, and this again leads to a potential disconnect between this metric and the ultimate criterion of user satisfaction.

Investigating which factors are potentially important for a positive user perception of a playlist, Lee conducted a qualitative user study [ 115 ], investigating playlists that had been automatically created based on content-based similarity . They made several interesting observations. A concern frequently raised by participants was that of consecutive songs being too similar, and a general lack of variety . However, different people had different interpretations of variety, e.g., variety in genres or styles vs. different artists in the playlist. Similarly, different criteria were mentioned when listeners judged the coherence of songs in a playlist, including lyrical content, tempo, and mood. When creating playlists, participants mentioned that similar lyrics, a common theme (e.g., music to listen to in the train), story (e.g., music for the Independence Day), or era (e.g., rock music from the 1980s) are important and that tracks not complying negatively effect the flow of the playlist. These aspects can be extended by responses of participants in a study conducted by Cunningham et al. [ 42 ], who further identified the following categories of playlists: same artist, genre, style, or orchestration, playlists for a certain event or activity (e.g., party or holiday), romance (e.g., love songs or breakup songs), playlists intended to send a message to their recipient (e.g., protest songs), and challenges or puzzles (e.g., cover songs liked more than the original or songs whose title contains a question mark).

Lee also found that personal preferences play a major role. In fact, already a single song that is very much liked or hated by a listener can have a strong influence on how they judge the entire playlist [ 115 ]. This seems particularly true if it is a highly disliked song [ 44 ]. Furthermore, a good mix of familiar and unknown songs was often mentioned as an important requirement for a good playlist. Supporting the discovery of interesting new songs, still contextualized by familiar ones, increases the likelihood of realizing a serendipitous encounter in a playlist [ 160 , 193 ]. Finally, participants also reported that their familiarity with a playlist’s genre or theme influenced their judgment of its quality. In general, listeners were more picky about playlists whose tracks they were familiar with or they liked a lot.

Supported by the studies summarized above, we argue that the question of what makes a great playlist is highly subjective and further depends on the intent of the creator or listener. Important criteria when creating or judging a playlist include track similarity/coherence, variety/diversity, but also the user’s personal preferences and familiarity with the tracks, as well as the intention of the playlist creator. Unfortunately, current automatic approaches to playlist continuation are agnostic of the underlying psychological and sociological factors that influence the decision of which songs users choose to include in a playlist. Since knowing about such factors is vital to understand the intent of the playlist creator, we believe that algorithmic methods for APC need to holistically learn such aspects from manually created playlists and integrate respective intent models. However, we are aware that in today’s era where billions of playlists are shared by users of online streaming services, Footnote 15 a large-scale analysis of psychological and sociological background factors is impossible. Nevertheless, in the absence of explicit information about user intent, a possible starting point to create intent models might be the metadata associated with user-generated playlists, such as title or description. To foster this kind of research, the playlists provided in the dataset for the ACM Recommender Systems Challenge 2018 include playlist titles. Footnote 16

2.4 Challenge 3: Evaluating music recommender systems

Problem definition   Having its roots in machine learning (cf. rating prediction) and information retrieval (cf. “retrieving” items based on implicit “queries” given by user preferences), the field of recommender systems originally adopted evaluation metrics from these neighboring fields. In fact, accuracy and related quantitative measures, such as precision, recall, or error measures (between predicted and true ratings), are still the most commonly employed criteria to judge the recommendation quality of a recommender system [ 11 , 78 ]. In addition, novel measures that are tailored to the recommendation problem have emerged in recent years. These so-called beyond-accuracy measures  [ 98 ] address the particularities of recommender systems and gauge, for instance, the utility, novelty, or serendipity of an item. However, a major problem with these kinds of measures is that they integrate factors that are hard to describe mathematically, for instance, the aspect of surprise in case of serendipity measures. For this reason, there sometimes exist a variety of different definitions to quantify the same beyond-accuracy aspect.

State of the art   In the following, we discuss performance measures which are most frequently reported when evaluating recommender systems. An overview of these is given in Table  1 . They can be roughly categorized into accuracy-related measures, such as prediction error (e.g., MAE and RMSE) or standard IR measures (e.g., precision and recall), and beyond-accuracy measures, such as diversity, novelty, and serendipity. Furthermore, while some of the metrics quantify the ability of recommender systems to find good items, e.g., precision and recall, others consider the ranking of items and therefore assess the system’s ability to position good recommendations at the top of the recommendation list, e.g., MAP, NDCG, or MPR.

Mean absolute error ( MAE ) is one of the most common metrics for evaluating the prediction power of recommender algorithms. It computes the average absolute deviation between the predicted ratings and the actual ratings provided by users [ 81 ]. Indeed, MAE indicates how close the rating predictions generated by an MRS are to the real user ratings. MAE is computed as follows:

where \(r_{u,i}\) and \(\hat{r}_{u,i}\) , respectively, denote the actual and the predicted ratings of item i for user u . MAE sums over the absolute prediction errors for all ratings in a test set T .

Root-mean-square error ( RMSE ) is another similar metric that is computed as:

It is an extension to MAE in that the error term is squared, which penalizes larger differences between predicted and true ratings more than smaller ones. This is motivated by the assumption that, for instance, a rating prediction of 1 when the true rating is 4 is much more severe than a prediction of 3 for the same item.

Precision at top K recommendations ( P@K ) is a common metric that measures the accuracy of the system in commanding relevant items. In order to compute P @ K , for each user, the top K recommended items whose ratings also appear in the test set T are considered. This metric was originally designed for binary relevance judgments. Therefore, in case of availability of relevance information at different levels, such as a five-point Likert scale, the labels should be binarized, e.g., considering the ratings greater than or equal to 4 (out of  5) as relevant. For each user u , \(P_u@K\) is computed as follows:

where \(L_u\) is the set of relevant items for user u in the test set T and \(\hat{L}_u\) denotes the recommended set containing the K items in T with the highest predicted ratings for the user u . The overall P @ K is then computed by averaging \(P_u@K\) values for all users in the test set.

Mean average precision at top K recommendations (MAP@K) is a rank-based metric that computes the overall precision of the system at different lengths of recommendation lists. MAP is computed as the arithmetic mean of the average precision over the entire set of users in the test set. Average precision for the top K recommendations ( AP @ K ) is defined as follows:

where rel ( i ) is an indicator signaling if the \(i^{\mathrm {th}}\) recommended item is relevant, i.e.,  \(rel(i)=1\) , or not, i.e.,  \(rel(i)=0\) ; N is the total number of relevant items. Note that MAP implicitly incorporates recall, because it also considers the relevant items not in the recommendation list. Footnote 17

Recall at top K recommendations (R@K) is presented here for the sake of completeness, even though it is not a crucial measure from a consumer’s perspective. Indeed, the listener is typically not interested in being recommended all or a large number of relevant items, rather in having good recommendations at the top of the recommendation list. For a user u , \(R_u@K\) is defined as:

where \(L_u\) is the set of relevant items of user u in the test set T and \(\hat{L}_u\) denotes the recommended set containing the K items in T with the highest predicted ratings for the user u . The overall R @ K is calculated by averaging \(R_u@K\) values for all the users in the test set.

Normalized discounted cumulative gain ( NDCG ) is a measure for the ranking quality of the recommendations. This metric has originally been proposed to evaluate the effectiveness of information retrieval systems [ 93 ]. It is nowadays also frequently used for evaluating music recommender systems [ 120 , 139 , 185 ]. Assuming that the recommendations for user u are sorted according to the predicted rating values in descending order. \(DCG_u\) is defined as follows:

where \(r_{u,i}\) is the true rating (as found in test set T ) for the item ranked at position i for user u , and N is the length of the recommendation list. Since the rating distribution depends on the users’ behavior, the DCG values for different users are not directly comparable. Therefore, the cumulative gain for each user should be normalized. This is done by computing the ideal DCG for user u , denoted as \(IDCG_u\) , which is the \(DCG_u\) value for the best possible ranking, obtained by ordering the items by true ratings in descending order. Normalized discounted cumulative gain for user u is then calculated as:

Finally, the overall normalized discounted cumulative gain \(N\!DCG\) is computed by averaging \(N\!DCG_u\) over the entire set of users.

In the following, we present common quantitative evaluation metrics, which have been particularly designed or adopted to assess recommender systems performance, even though some of them have their origin in information retrieval and machine learning. The first two (HLU and MRR) still belong to the category of accuracy-related measures, while the subsequent ones capture beyond-accuracy aspects

Half-life utility ( HLU ) measures the utility of a recommendation list for a user with the assumption that the likelihood of viewing/choosing a recommended item by the user exponentially decays with the item’s position in the ranking [ 24 , 137 ]. Formally written, HLU for user u is defined as:

where \(r_{u,i}\) and \(rank_{u,i}\) denote the rating and the rank of item i for user u , respectively, in the recommendation list of length N ; d represents a default rating (e.g., average rating); and h is the half-time, calculated as the rank of a music item in the list, such that the user can eventually listen to it with a 50% chance. \(HLU_u\) can be further normalized by the maximum utility (similar to NDCG), and the final HLU is the average over the half-time utilities obtained for all users in the test set. A larger HLU may correspond to a superior recommendation performance.

Mean percentile rank ( MPR ) estimates the users’ satisfaction with items in the recommendation list and is computed as the average of the percentile rank for each test item within the ranked list of recommended items for each user [ 89 ]. The percentile rank of an item is the percentage of items whose position in the recommendation list is equal to or lower than the position of the item itself. Formally, the percentile rank \(PR_u\) for user u is defined as:

where \(r_{u,i}\) is the true rating (as found in test set T ) for item i rated by user u and \(rank_{u,i}\) is the percentile rank of item i within the ordered list of recommendations for user u . MPR is then the arithmetic mean of the individual \(PR_u\) values over all users. A randomly ordered recommendation list has an expected MPR value of 50%. A smaller MPR value is therefore assumed to correspond to a superior recommendation performance.

Spread is a metric of how well the recommender algorithm can spread its attention across a larger set of items [ 104 ]. In more detail, spread is the entropy of the distribution of the items recommended to the users in the test set. It is formally defined as:

where I represents the entirety of items in the dataset and \(P(i) = count(i) / \sum _{i' \in I}{count(i')}\) , such that count ( i ) denotes the total number of times that a given item i showed up in the recommendation lists. It may be infeasible to expect an algorithm to achieve the perfect spread (i.e., recommending each item an equal number of times) without avoiding irrelevant recommendations or unfulfillable rating requests. Accordingly, moderate spread values are usually preferable.

Coverage of a recommender system is defined as the proportion of items over which the system is capable of generating recommendations [ 81 ]:

where | T | is the size of the test set and \(|\hat{T}|\) is the number of ratings in T for which the system can predict a value. This is particularly important in cold start situations, when recommender systems are not able to accurately predict the ratings of new users or new items and hence obtain low coverage. Recommender systems with lower coverage are therefore limited in the number of items they can recommend. A simple remedy to improve low coverage is to implement some default recommendation strategy for an unknown user–item entry. For example, we can consider the average rating of users for an item as an estimate of its rating. This may come at the price of accuracy, and therefore, the trade-off between coverage and accuracy needs to be considered in the evaluation process [ 7 ].

Novelty measures the ability of a recommender system to recommend new items that the user did not know about before [ 1 ]. A recommendation list may be accurate, but if it contains a lot of items that are not novel to a user, it is not necessarily a useful list [ 193 ].

While novelty should be defined on an individual user level, considering the actual freshness of the recommended items, it is common to use the self-information of the recommended items relative to their global popularity:

where \(pop_i\) is the popularity of item i measured as percentage of users who rated i , \(L_u\) is the recommendation list of the top N recommendations for user u  [ 193 , 195 ]. The above definition assumes that the likelihood of the user selecting a previously unknown item is proportional to its global popularity and is used as an approximation of novelty. In order to obtain more accurate information about novelty or freshness, explicit user feedback is needed, in particular since the user might have listened to an item through other channels before.

It is often assumed that the users prefer recommendation lists with more novel items. However, if the presented items are too novel, then the user is unlikely to have any knowledge of them, nor to be able to understand or rate them. Therefore, moderate values indicate better performances [ 104 ].

Serendipity aims at evaluating MRS based on the relevant and surprising recommendations. While the need for serendipity is commonly agreed upon [ 82 ], the question of how to measure the degree of serendipity for a recommendation list is controversial. This particularly holds for the question of whether the factor of surprise implies that items must be novel to the user [ 98 ]. On a general level, serendipity of a recommendation list \(L_u\) provided to a user u can be defined as:

where \(L_u^{unexp}\) and \(L_u^{useful}\) denote subsets of L that contain, respectively, recommendations unexpected to and useful for the user. The usefulness of an item is commonly assessed by explicitly asking users or taking user ratings as proxy [ 98 ]. The unexpectedness of an item is typically quantified by some measure of distance from expected items, i.e., items that are similar to the items already rated by the user. In the context of MRS, Zhang et al. [ 193 ] propose an “unserendipity” measure that is defined as the average similarity between the items in the user’s listening history and the new recommendations. Similarity between two items in this case is calculated by an adapted cosine measure that integrates co-liking information, i.e., number of users who like both items. It is assumed that lower values correspond to more surprising recommendations, since lower values indicate that recommendations deviate from the user’s traditional behavior [ 193 ].

Diversity is another beyond-accuracy measure as already discussed in the limitations part of Challenge 1. It gauges the extent to which recommended items are different from each other, where difference can relate to various aspects, e.g., musical style, artist, lyrics, or instrumentation, just to name a few. Similar to serendipity, diversity can be defined in several ways. One of the most common is to compute pairwise distance between all items in the recommendation set, either averaged [ 196 ] or summed [ 173 ]. In the former case, the diversity of a recommendation list L is calculated as follows:

where \(dist_{i,j}\) is the some distance function defined between items i and j . Common choices are inverse cosine similarity [ 150 ], inverse Pearson correlation [ 183 ], or Hamming distance [ 101 ].

When it comes to the task of evaluating playlist recommendation, where the goal is to assess the capability of the recommender in providing proper transitions between subsequent songs, the conventional error or accuracy metrics may not be able to capture this property. There is hence a need for sequence-aware evaluation measures. For example, consider the scenario where a user who likes both classical and rock music is recommended a rock music right after she has listened to a classic piece. Even though both music styles are in agreement with her taste, the transition between songs plays an important role toward user satisfaction. In such a situation, given a currently played song and in the presence of several equally likely good options to be played next, a RS may be inclined to rank songs based on their popularity. Hence, other metrics such as average log-likelihood have been proposed to better model the transitions [ 33 , 34 ]. In this regard, when the goal is to suggest a sequence of items, alternative multi-metric evaluation approaches are required to take into consideration multiple quality factors. Such evaluation metrics can consider the ranking order of the recommendations or the internal coherence or diversity of the recommended list as a whole. In many scenarios, adoption of such quality metrics can lead to a trade-off with accuracy which should be balanced by the RS algorithm [ 145 ].

Limitations   As of today, the vast majority of evaluation approaches in recommender systems research focus on quantitative measures, either accuracy-like or beyond-accuracy, which are often computed in offline studies.

Doing so has the advantage of facilitating the reproducibility of evaluation results. However, limiting the evaluation to quantitative measures means to forgo another important factor, which is user experience. In other words, in the absence of user-centric evaluations, it is difficult to extend the claims to the more important objective of the recommender system under evaluation, i.e., giving users a pleasant and useful personalized experience [ 107 ].

Despite acknowledging the need for more user-centric evaluation strategies [ 158 ], the factor human, user, or, in the case of MRS, listener is still way too often neglected or not properly addressed. For instance, while there exist quantitative objective measures for serendipity and diversity, as discussed above, perceived serendipity and diversity can be highly different from the measured ones [ 182 ] as they are subjective user-specific concepts. This illustrates that even beyond-accuracy measures cannot fully capture the real user satisfaction with a recommender system. On the other hand, approaches that address user experience (UX) can be investigated to evaluate recommender systems. For example, a MRS can be evaluated based on user engagement , which provides a restricted explanation of UX that concentrates on judgment of product quality during interaction [ 79 , 118 , 133 ]. User satisfaction, user engagement, and more generally user experience are commonly assessed through user studies [ 13 , 116 , 117 ].

Addressing both objective and subjective evaluation criteria, Knijnenburg et al. [ 108 ] propose a holistic framework for user-centric evaluation of recommender systems. Figure  1 provides an overview of the components. The objective system aspects (OSAs) are considered unbiased factors of the RS, including aspects of the user interface, computing time of the algorithm, or number of items shown to the user. They are typically easy to specify or compute. The OSAs influence the subjective system aspects (SSAs), which are caused by momentary, primary evaluative feelings while interacting with the system [ 80 ]. This results in a different perception of the system by different users. SSAs are therefore highly individual aspects and typically assessed by user questionnaires. Examples of SSA include general appeal of the system, usability, and perceived recommendation diversity or novelty. The aspect of experience (EXP) describes the user’s attitude toward the system and is commonly also investigated by questionnaires. It addresses the user’s perception of the interaction with the system. The experience is highly influenced by the other components, which means changing any of the other components likely results in a change of EXP aspects. Experience can be broken down into the evaluation of the system, the decision process, and the final decisions made, i.e., the outcome. The interaction (INT) aspects describe the observable behavior of the user, time spent viewing an item, as well as clicking or purchasing behavior. In a music context, examples further include liking a song or adding it to a playlist. Therefore, interactions aspects belong to the objective measures and are usually determined via logging by the system. Finally, Knijnenburg et al.’s framework mentions personal characteristics (PC) and situational characteristics (SC), which influence the user experience. PC include aspects that do not exist without the user, such as user demographics, knowledge, or perceived control, while SC include aspects of the interaction context, such as when and where the system is used, or situation-specific trust or privacy concerns. Knijnenburg et al. [ 108 ] also propose a questionnaire to asses the factors defined in their framework, for instance, perceived recommendation quality, perceived system effectiveness, perceived recommendation variety, choice satisfaction, intention to provide feedback, general trust in technology, and system-specific privacy concern.

While this framework is a generic one, tailoring it to MRS would allow for user-centric evaluation thereof. In particular, the aspects of personal and situational characteristics should be adapted to the particularities of music listeners and listening situations, respectively, cf. Sect.  2.1 . To this end, researchers in MRS should consider the aspects relevant to the perception and preference of music, and their implications on MRS, which have been identified in several studies, e.g., [ 43 , 113 , 114 , 158 , 159 ]. In addition to the general ones mentioned by Knijnenburg et al., of great importance in the music domain seem to be psychological factors, including affect and personality, social influence, musical training and experience, and physiological condition.

We believe that carefully and holistically evaluating MRS by means of accuracy and beyond-accuracy, objective and subjective measures, in offline and online experiments, would lead to a better understanding of the listeners’ needs and requirements vis-à-vis MRS, and eventually a considerable improvement of current MRS.

3 Future directions and visions

While the challenges identified in the previous section are already researched on intensely, in the following, we provide a more forward-looking analysis and discuss some MRS-related trending topics, which we assume influential for the next generation of MRS. All of them have in common that their aim is to create more personalized recommendations. More precisely, we first outline how psychological constructs such as personality and emotion could be integrated into MRS. Subsequently, we address situation-aware MRS and argue for the need of multifaceted user models that describe contextual and situational preferences. To round off, we discuss the influence of users’ cultural background on recommendation preferences, which needs to be considered when building culture-aware MRS.

3.1 Psychologically inspired music recommendation

Personality and emotion are important psychological constructs. While personality characteristics of humans are a predictable and stable measure that shapes human behaviors, emotions are short-term affective responses to a particular stimulus [ 179 ]. Both have been shown to influence music tastes [ 71 , 154 , 159 ] and user requirements for MRS [ 69 , 73 ]. However, in the context of (music) recommender systems, personality and emotion do not play a major role yet. Given the strong evidence that both influence listening preferences [ 147 , 159 ] and the recent emergence of approaches to accurately predict them from user-generated data [ 111 , 170 ], we believe that psychologically inspired MRS is an upcoming area.

3.1.1 Personality

In psychology research, personality is often defined as a “consistent behavior pattern and interpersonal processes originating within the individual” [ 25 ]. This definition accounts for the individual differences in people’s emotional, interpersonal, experiential, attitudinal, and motivational styles [ 95 ]. Several prior works have studied the relation of decision making and personality factors. In [ 147 ], as an example, it has been shown that personality can influence the human decision-making process as well as the tastes and interests. Due to this direct relation, people with similar personality factors are very likely to share similar interests and tastes.

Earlier studies conducted on the user personality characteristics support the potential benefits that personality information could have in recommender systems [ 22 , 23 , 58 , 85 , 87 , 178 , 180 ]. As a known example, psychological studies [ 147 ] have shown that extravert people are likely to prefer the upbeat and conventional music. Accordingly, a personality-based MRS could use this information to better predict which songs are more likely than others to please extravert people [ 86 ]. Another example of potential usage is to exploit personality information in order to compute similarity among users and hence identify the like-minded users [ 178 ]. This similarity information could then be integrated into a neighborhood-based collaborative filtering approach.

In order to use personality information in a recommender system, the system first has to elicit this information from the users, which can be done either explicitly or implicitly. In the former case, the system can ask the user to complete a personality questionnaire using one of the personality evaluation inventories, e.g., the ten- item personality inventory [ 76 ] or the big five inventory [ 94 ]. In the latter case, the system can learn the personality by tracking and observing users’ behavioral patterns, for instance, liking behavior on Facebook [ 111 ] or applying filters to images posted on Instagram [ 170 ]. Not too surprisingly, it has shown that systems that explicitly elicit personality characteristics achieve superior recommendation outcomes, e.g., in terms of user satisfaction, ease of use, and prediction accuracy [ 52 ]. On the downside, however, many users are not willing to fill in long questionnaires before being able to use the RS. A way to alleviate this problem is to ask users only the most informative questions of a personality instrument [ 163 ]. Which questions are most informative, though, first needs to be determined based on existing user data and is dependent on the recommendation domain. Other studies showed that users are to some extent willing to provide further information in return for a better quality of recommendations [ 175 ].

Evaluation framework of the user experience for recommender systems, according to [ 108 ]

Personality information can be used in various ways, particularly, to generate recommendations when traditional rating or consumption data is missing. Otherwise, the personality traits can be seen as an additional feature that extends the user profile, that can be used mainly to identify similar users in neighborhood-based recommender systems or directly fed into extended matrix factorization models [ 67 ].

3.1.2 Emotion

The emotional state of the MRS user has a strong impact on his or her short-time musical preferences [ 99 ]. Vice versa, music has a strong influence on our emotional state. It therefore does not come as a surprise that emotion regulation was identified as one of the main reasons why people listen to music [ 122 , 155 ]. As an example, people may listen to completely different musical genres or styles when they are sad in comparison with when they are happy. Indeed, prior research on music psychology discovered that people may choose the type of music which moderates their emotional condition [ 109 ]. More recent findings show that music can be mainly chosen so as to augment the emotional situation perceived by the listener [ 131 ]. In order to build emotion-aware MRS, it is therefore necessary to (i) infer the emotional state the listener is in, (ii) infer emotional concepts from the music itself, and (iii) understand how these two interrelate. These three tasks are detailed below.

Eliciting the emotional state of the listener Similar to personality traits, the emotional state of a user can be elicited explicitly or implicitly. In the former case, the user is typically presented one of the various categorical models (emotions are described by distinct emotion words such as happiness, sadness, anger, or fear) [ 84 , 191 ] or dimensional models (emotions are described by scores with respect to two or three dimensions, e.g., valence and arousal) [ 152 ]. For a more detailed elaboration on emotion models in the context of music, we refer to [ 159 , 186 ]. The implicit acquisition of emotional states can be effected, for instance, by analyzing user-generated text [ 49 ], speech [ 66 ], or facial expressions in video [ 55 ].

Emotion tagging in music The music piece itself can be regarded as an emotion-laden content and in turn can be described by emotion words. The task of automatically assigning such emotion words to a music piece is an active research area, often refereed to as music emotion recognition (MER), e.g., [ 14 , 91 , 103 , 187 , 188 , 191 ]. How to integrate such emotion terms created by MER tools into a MRS is, however, not an easy task, for several reasons. First, early MER approaches usually neglected the distinction between intended emotion , perceived emotion , and induced or felt emotion , cf. Sect.  2.1 . Current MER approaches focus on perceived or induced emotions. However, musical content still contains various characteristics that affect the emotional state of the listener, such as lyrics, rhythm, and harmony, and the way how they affect the emotional state is highly subjective. This so even though research has detected a few general rules, for instance, a musical piece that is in major key is typically perceived brighter and happier than those in minor key, or a piece in rapid tempo is perceived more exciting or more tense than slow tempo ones [ 112 ].

Connecting listener emotions and music emotion tags Current emotion-based MRSs typically consider emotional scores as contextual factors that characterize the situation the user is experiencing. Hence, the recommender systems exploit emotions in order to pre-filter the preferences of users or post-filter the generated recommendations. Unfortunately, this neglects the psychological background, in particular on the subjective and complex interrelationships between expressed, perceived, and induced emotions [ 159 ], which is of special importance in the music domain as music is known to evoke stronger emotions than, for instance, products [ 161 ]. It has also been shown that personality influences in which emotional state which kind of emotionally laden music is preferred by listeners [ 71 ]. Therefore, even if automated MER approaches would be able to accurately predict the perceived or induced emotion of a given music piece, in the absence of deep psychological listener profiles, matching emotion annotations of items and listeners may not yield satisfying recommendations. This is so because how people judge music and which kind of music they prefer depends to a large extent on their current psychological and cognitive states. We hence believe that the field of MRS should embrace psychological theories, elicit the respective user-specific traits, and integrate them into recommender systems, in order to build decent emotion-aware MRS.

3.2 Situation-aware music recommendation

Most of the existing music recommender systems make recommendations solely based on a set of user-specific and item-specific signals. However, in real-world scenarios, many other signals are available. These additional signals can be further used to improve the recommendation performance. A large subset of these additional signals includes situational signals . In more detail, the music preference of a user depends on the situation at the moment of recommendation. Footnote 18 Location is an example of situational signals; for instance, the music preference of a user would differ in libraries and in gyms [ 35 ]. Therefore, considering location as a situation-specific signal could lead to substantial improvements in the recommendation performance. Time of the day is another situational signal that could be used for recommendation; for instance, the music a user would like to listen to in mornings differs from those in nights [ 41 ]. One situational signal of particular importance in the music domain is social context since music tastes and consumption behaviors are deeply rooted in the users’ social identities and mutually affect each other [ 45 , 134 ]. For instance, it is very likely that a user would prefer different music when being alone than when meeting friends. Such social factors should therefore be considered when building situation-aware MRS. Other situational signals that are sometimes exploited include the user’s current activity  [ 184 ], the weather  [ 140 ], the user’s mood  [ 129 ], and the day of the week  [ 83 ]. Regarding time, there is also another factor to consider, which is that most music that was considered trendy years ago is now considered old. This implies that ratings for the same song or artist might strongly differ, not only between users, but in general as a function of time. To incorporate such aspects in MRS, it would be crucial to record a timestamp for all ratings.

It is worth noting that situational features have been proven to be strong signals in improving retrieval performance in search engines [ 16 , 190 ]. Therefore, we believe that researching and building situation-aware music recommender systems should be one central topic in MRS research.

While several situation-aware MRSs already exist, e.g., [ 12 , 35 , 90 , 100 , 157 , 184 ], they commonly exploit only one or very few such situational signals, or are restricted to a certain usage context, e.g., music consumption in a car or in a tourist scenario. Those systems that try to take a more comprehensive view and consider a variety of different signals, on the other hand, suffer from a low number of data instances or users, rendering it very hard to build accurate context models [ 75 ]. What is still missing, in our opinion, are (commercial) systems that integrate a variety of situational signals on a very large scale in order to truly understand the listeners needs and intents in any given situation and recommend music accordingly. While we are aware that data availability and privacy concerns counteract the realization of such systems on a large commercial scale, we believe that MRS will eventually integrate decent multifaceted user models inferred from contextual and situational factors.

3.3 Culture-aware music recommendation

While most humans share an inclination to listen to music, independent on their location or cultural background, the way music is performed, perceived, and interpreted evolves in a culture-specific manner. However, research in MRS seems to be agnostic of this fact. In music information retrieval (MIR) research, on the other hand, cultural aspects have been studied to some extent in recent years, after preceding (and still ongoing) criticisms of the predominance of Western music in this community. Arguably the most comprehensive culture-specific research in this domain has been conducted as part of the CompMusic project, Footnote 19 in which five non-Western music traditions have been analyzed in detail in order to advance automatic description of music by emphasizing cultural specificity. The analyzed music traditions included Indian Hindustani and Carnatic [ 53 ], Turkish Makam [ 54 ], Arab-Andalusian [ 174 ], and Beijing Opera [ 148 ]. However, the project’s focus was on music creation, content analysis, and ethnomusicological aspects rather than on the music consumption side [ 37 , 165 , 166 ]. Recently, analyzing content-based audio features describing rhythm, timbre, harmony, and melody for a corpus of a larger variety of world and folk music with given country information, Panteli et al. found distinct acoustic patterns of the music created in individual countries [ 138 ]. They also identified geographical and cultural proximities that are reflected in music features, looking at outliers and misclassifications in a classification experiments using country as target class. For instance, Vietnamese music was often confused with Chinese and Japanese, South African with Botswanese.

In contrast to this—meanwhile quite extensive—work on culture-specific analysis of music traditions, little effort has been made to analyze cultural differences and patterns of music consumption behavior, which is, as we believe, a crucial step to build culture-aware MRS. The few studies investigating such cultural differences include [ 88 ], in which Hu and Lee found differences in perception of moods between American and Chinese listeners. By analyzing the music listening behavior of users from 49 countries, Ferwerda et al. found relationships between music listening diversity and Hofstede’s cultural dimensions [ 70 , 72 ]. Skowron et al. used the same dimensions to predict genre preferences of listeners with different cultural backgrounds [ 171 ]. Schedl analyzed a large corpus of listening histories created by Last.fm users in 47 countries and identified distinct preference patterns [ 156 ]. Further analyses revealed countries closest to what can be considered the global mainstream (e.g., the Netherlands, UK, and Belgium) and countries farthest from it (e.g., China, Iran, and Slovakia). However, all of these works define culture in terms of country borders, which often makes sense, but is sometimes also problematic, for instance, in countries with large minorities of inhabitants with different cultures.

In our opinion, when building MRS, the analysis of cultural patterns of music consumption behavior, subsequent creation of respective cultural listener models, and their integration into recommender systems are vital steps to improve personalization and serendipity of recommendations. Culture should be defined on various levels though, not only country borders. Other examples include having a joint historical background, speaking the same language, sharing the same beliefs or religion, and differences between urban vs. rural cultures. Another aspect that relates to culture is a temporal one since certain cultural trends, e.g., what defines the “youth culture,” are highly dynamic in a temporal and geographical sense. We believe that MRS which are aware of such cross-cultural differences and similarities in music perception and taste, and are able to recommend music a listener in the same or another culture may like, would substantially benefit both users and providers of MRS.

4 Conclusions

In this trends and survey paper, we identified several grand challenges the research field of music recommender systems (MRS) is facing. These are, among others, in the focus of current research in the area of MRS. We discussed (1) the cold start problem of items and users, with its particularities in the music domain, (2) the challenge of automatic playlist continuation , which is gaining importance due to the recently emerged user request of being recommended musical experiences rather than single tracks [ 161 ], and (3) the challenge of holistically evaluating music recommender systems, in particular, capturing aspects beyond accuracy.

In addition to the grand challenges, which are currently highly researched, we also presented a visionary outlook of what we believe to be the most interesting future research directions in MRS. In particular, we discussed (1) psychologically inspired MRS , which consider in the recommendation process factors such as listeners’ emotion and personality, (2) situation-aware MRS , which holistically model contextual and environmental aspects of the music consumption process, infer listener needs and intents, and eventually integrate these models at large scale in the recommendation process, and (3) culture-aware MRS , which exploit the fact that music taste highly depends on the cultural background of the listener, where culture can be defined in manifold ways, including historical, political, linguistic, or religious similarities.

We hope that this article helped pinpointing major challenges, highlighting recent trends, and identifying interesting research questions in the area of music recommender systems. Believing that research addressing the discussed challenges and trends will pave the way for the next generation of music recommender systems, we are looking forward to exciting, innovative approaches and systems that improve user satisfaction and experience, rather than just accuracy measures.

http://www.recsyschallenge.com/2018 .

http://www.cp.jku.at/tutorials/mrs_recsys_2017 .

Spotify reports about 30 million songs in 2017 ( https://press.spotify.com/at/about ); Amazon’s advanced search for books reports 10 million hardcover and 30 million paperback books in 2017 ( https://www.amazon.com/Advanced-Search-Books/b?node=241582011 ), whereas Netflix, in contrast, offers about 5,500 movies and TV series as of 2016 ( http://time.com/4272360/the-number-of-movies-on-netflix-is-dropping-fast ).

Please note that the terms “emotion” and “mood” have different meanings in psychology, whereas they are commonly used as synonyms in music information retrieval (MIR) and recommender systems research. In psychology, in contrast, “emotion” refers to a short-time reaction to a particular stimulus, whereas “mood” refers to a longer-lasting state without relation to a specific stimulus.

Note that Dror et al.’s analysis was conducted in 2011. Even though the general character (rating matrices for music items being sparser than those of movie items) remained the same, the actual numbers for today’s catalogs are likely slightly different.

http://www.pandora.com/about/mgp .

http://enacademic.com/dic.nsf/enwiki/3224302 .

https://musicbiz.org/news/playlists-overtake-albums-listenership-says-loop-study .

https://musicbiz.org/resources/tools/music-biz-consumer-insights/consumer-insights-portal .

https://www.midiaresearch.com/blog/announcing-midias-state-of-the-streaming-nation-2-report .

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The ranking of criteria (from most to least important) was: homogeneity, artist diversity, transition, popularity, lyrics, order, and freshness.

https://www.spotify.com/discoverweekly .

https://recsys-challenge.spotify.com .

We should note that in the recommender systems community, another variation of average precision is gaining popularity recently, formally defined by: \(AP@K = \frac{1}{\min (K,N)} \sum _{i=1}^{K} {P@i \, \cdot \, rel(k)}\) in which N is the total number of relevant items and K is the size of recommendation list. The motivation behind the minimization term is to prevent the AP scores to be unfairly suppressed when the number of recommendations is too low to capture all the relevant items. This variation of MAP was popularized by Kaggle competitions [ 97 ] about recommender systems and has been used in several other research works, consider for example [ 8 , 124 ].

Please note that music taste is a relatively stable characteristic, while music preferences vary depending on the context and listening intent.

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Acknowledgements

Open access funding provided by Johannes Kepler University Linz. We would like to thank all researchers in the fields of recommender systems, information retrieval, music research, and multimedia, with whom we had the pleasure to discuss and collaborate in recent years, and whom in turn influenced and helped shaping this article. Special thanks go to Peter Knees and Fabien Gouyon for the fruitful discussions while preparing the ACM Recommender Systems 2017 tutorial on music recommender systems. In addition, we would like to thank the reviewers of our manuscript, who provided useful and constructive comments to improve the original draft and turn it into what it is now. We would also like to thank Eelco Wiechert for providing additional pointers to relevant literature. Furthermore, the many personal discussions with actual users of MRS unveiled important shortcomings of current approaches and in turn were considered in this article.

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Hamed Zamani

Spotify USA Inc., New York, USA

Ching-Wei Chen

Department of Computer Science, Politecnico di Milano, Milan, Italy

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Free University of Bozen-Bolzano, Bolzano, Italy

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Schedl, M., Zamani, H., Chen, CW. et al. Current challenges and visions in music recommender systems research. Int J Multimed Info Retr 7 , 95–116 (2018). https://doi.org/10.1007/s13735-018-0154-2

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Received : 28 September 2017

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DOI : https://doi.org/10.1007/s13735-018-0154-2

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Effects of music therapy on depression: A meta-analysis of randomized controlled trials

Qishou tang.

1 Bengbu Medical University, Bengbu, Anhui, China

Zhaohui Huang

2 Anhui Provincial Center for Women and Child Health, Hefei, Anhui, China

3 National Drug Clinical Trial Institution, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China

Associated Data

All relevant data are within the manuscript and its Supporting Information files.

We aimed to determine and compare the effects of music therapy and music medicine on depression, and explore the potential factors associated with the effect.

PubMed (MEDLINE), Ovid-Embase, the Cochrane Central Register of Controlled Trials, EMBASE, Web of Science, and Clinical Evidence were searched to identify studies evaluating the effectiveness of music-based intervention on depression from inception to May 2020. Standardized mean differences (SMDs) were estimated with random-effect model and fixed-effect model.

A total of 55 RCTs were included in our meta-analysis. Music therapy exhibited a significant reduction in depressive symptom (SMD = −0.66; 95% CI = -0.86 to -0.46; P <0.001) compared with the control group; while, music medicine exhibited a stronger effect in reducing depressive symptom (SMD = −1.33; 95% CI = -1.96 to -0.70; P <0.001). Among the specific music therapy methods, recreative music therapy (SMD = -1.41; 95% CI = -2.63 to -0.20; P <0.001), guided imagery and music (SMD = -1.08; 95% CI = -1.72 to -0.43; P <0.001), music-assisted relaxation (SMD = -0.81; 95% CI = -1.24 to -0.38; P <0.001), music and imagery (SMD = -0.38; 95% CI = -0.81 to 0.06; P = 0.312), improvisational music therapy (SMD = -0.27; 95% CI = -0.49 to -0.05; P = 0.001), music and discuss (SMD = -0.26; 95% CI = -1.12 to 0.60; P = 0.225) exhibited a different effect respectively. Music therapy and music medicine both exhibited a stronger effects of short and medium length compared with long intervention periods.

Conclusions

A different effect of music therapy and music medicine on depression was observed in our present meta-analysis, and the effect might be affected by the therapy process.

Introduction

Depression was reported to be a common mental disorders and affected more than 300 million people worldwide, and long-lasting depression with moderate or severe intensity may result in serious health problems [ 1 ]. Depression has become the leading causes of disability worldwide according to the recent World Health Organization (WHO) report. Even worse, depression was closely associated with suicide and became the second leading cause of death, and nearly 800 000 die of depression every year worldwide [ 1 , 2 ]. Although it is known that treatments for depression, more than 3/4 of people in low and middle-income income countries receive no treatment due to a lack of medical resources and the social stigma of mental disorders [ 3 ]. Considering the continuously increased disease burden of depression, a convenient effective therapeutic measures was needed at community level.

Music-based interventions is an important nonpharmacological intervention used in the treatment of psychiatric and behavioral disorders, and the obvious curative effect on depression has been observed. Prior meta-analyses have reported an obvious effect of music therapy on improving depression [ 4 , 5 ]. Today, it is widely accepted that the music-based interventions are divided into two major categories, namely music therapy and music medicine. According to the American Music Therapy Association (AMTA), “music therapy is the clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed professional who has completed an approved music therapy program” [ 6 ]. Therefore, music therapy is an established health profession in which music is used within a therapeutic relationship to address physical, emotional, cognitive, and social needs of individuals, and includes the triad of music, clients and qualified music therapists. While, music medicine is defined as mainly listening to prerecorded music provided by medical personnel or rarely listening to live music. In other words, music medicine aims to use music like medicines. It is often managed by a medical professional other than a music therapist, and it doesn’t need a therapeutic relationship with the patients. Therefore, the essential difference between music therapy and music medicine is about whether a therapeutic relationship is developed between a trained music therapist and the client [ 7 – 9 ]. In the context of the clear distinction between these two major categories, it is clear that to evaluate the effects of music therapy and other music-based intervention studies on depression can be misleading. While, the distinction was not always clear in most of prior papers, and no meta-analysis comparing the effects of music therapy and music medicine was conducted. Just a few studies made a comparison of music-based interventions on psychological outcomes between music therapy and music medicine. We aimed to (1) compare the effect between music therapy and music medicine on depression; (2) compare the effect between different specific methods used in music therapy; (3) compare the effect of music-based interventions on depression among different population [ 7 , 8 ].

Materials and methods

Search strategy and selection criteria.

PubMed (MEDLINE), Ovid-Embase, the Cochrane Central Register of Controlled Trials, EMBASE, Web of Science, and Clinical Evidence were searched to identify studies assessing the effectiveness of music therapy on depression from inception to May 2020. The combination of “depress*” and “music*” was used to search potential papers from these databases. Besides searching for electronic databases, we also searched potential papers from the reference lists of included papers, relevant reviews, and previous meta-analyses. The criteria for selecting the papers were as follows:(1) randomised or quasi-randomised controlled trials; (2) music therapy at a hospital or community, whereas the control group not receiving any type of music therapy; (3) depression rating scale was used. The exclusive criteria were as follows: (1) non-human studies; (2) studies with a very small sample size (n<20); (3) studies not providing usable data (including sample size, mean, standard deviation, etc.); (4) reviews, letters, protocols, etc. Two authors independently (YPJ, HZH) searched and screened the relevant papers. EndNote X7 software was utilized to delete the duplicates. The titles and abstracts of all searched papers were checked for eligibility. The relevant papers were selected, and then the full-text papers were subsequently assessed by the same two authors. In the last, a panel meeting was convened for resolving the disagreements about the inclusion of the papers.

Data extraction

We developed a data abstraction form to extract the useful data: (1) the characteristics of papers (authors, publish year, country); (2) the characteristics of participators (sample size, mean age, sex ratio, pre-treatment diagnosis, study period); (3) study design (random allocation, allocation concealment, masking, selection process of participators, loss to follow-up); (4) music therapy process (music therapy method, music therapy period, music therapy frequency, minutes per session, and the treatment measures in the control group); (5) outcome measures (depression score). Two authors independently (TQS, ZH) abstracted the data, and disagreements were resolved by discussing with the third author (YPJ).

Assessment of risk of bias in included studies

Two authors independently (TQS, ZH) assessed the risk of bias of included studies using Cochrane Collaboration’s risk of bias assessment tool, and disagreements were resolved by discussing with the third author (YPJ) [ 10 ].

Music therapy and music medicine

Music Therapy is defined as the clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed professional who has completed an approved music therapy program. Music medicine is defined as mainly listening to prerecorded music provided by medical personnel or rarely listening to live music. In other words, music medicine aims to use music like medicines.

Music therapy mainly divided into active music therapy and receptive music therapy. Active music therapy, including improvisational, re-creative, and compositional, is defined as playing musical instruments, singing, improvisation, and lyrics of adaptation. Receptive music therapy, including music-assisted relaxation, music and imagery, guided imagery and music, lyrics analysis, and so on, is defined as music listening, lyrics analysis, and drawing with musing. In other words, in active methods participants are making music, and in receptive music therapy participants are receiving music [ 6 , 7 , 9 , 11 – 13 ].

Evaluation of depression

Depression was evaluated by the common psychological scales, including Beck Depression Inventory (BDI), Children’s Depression Inventory (CDI), Center for Epidemiologic Studies Depression (CES-D), Cornell Scale (CS), Depression Mood Self-Report Inventory for Adolescence (DMSRIA), Geriatric Depression Scale-15 (GDS-15); Geriatric Depression Scale-30 (GDS-30), Hospital Anxiety and Depression Scale (HADS), Hamilton Rating Scale for Depression (HRSD/HAMD), Montgomery-sberg Depression Rating Scale (MADRS), Patient Reported Outcomes Measurement Information System (PROMIS), Self-Rating Depression Scale (SDS), Short Version of Profile of Mood States (SV-POMS).

Statistical analysis

The pooled effect were estimated by using the standardized mean differences (SMDs) and its 95% confidence interval (95% CI) due to the different depression rate scales were used in the included papers. Heterogeneity between studies was assessed by I-square ( I 2 ) and Q-statistic (P<0.10), and a high I 2 (>50%) was recognized as heterogeneity and a random-effect model was used [ 14 – 16 ]. We performed subgroup analyses and meta-regression analyses to study the potential heterogeneity between studies. The subgroup variables included music intervention categories (music therapy and music medicine), music therapy methods (active music therapy, receptive music therapy), specific receptive music therapy methods (music-assisted relaxation, music and imagery, and guided imagery and music (Bonny Method), specific active music therapy methods (recreative music therapy and improvisational music therapy), music therapy mode (group therapy, individual therapy), music therapy period (weeks) (2–4, 5–12, ≥13), music therapy frequency (once weekly, twice weekly, ≥3 times weekly), total music therapy sessions (1–4, 5–8, 9–12, 13–16, >16), time per session (minutes) (15–40, 41–60, >60), inpatient settings (secure [locked] unit at a mental health facility versus outpatient settings), sample size (20–50, ≥50 and <100, ≥100), female predominance(>80%) (no, yes), mean age (years) (<50, 50–65, >65), country having music therapy profession (no, yes), pre-treatment diagnosis (mental health, depression, severe mental disease/psychiatric disorder). We also performed sensitivity analyses to test the robustness of the results by re-estimating the pooled effects using fixed effect model, using trim and fill analysis, excluding the paper without information on music therapy, excluding the papers with more high biases, excluding the papers with small sample size (20< n<30), excluding the papers using an infrequently used scale, excluding the studies focused on the people with a severe mental disease. We investigated the publication biases by a funnel plot as well as Egger’s linear regression test [ 17 ]. The analyses were performed using Stata, version 11.0. All P-values were two-sided. A P-value of less than 0.05 was considered to be statistically significant.

Characteristics of the eligible studies

Fig 1 depicts the study profile, and a total of 55 RCTs were included in our meta-analysis [ 18 – 72 ]. Of the 55 studies, 10 studies from America, 22 studies from Europe, 22 studies from Asia, and 1 study from Australia. The mean age of the participators ranged from 12 to 86; the sample size ranged from 20 to 242. A total of 16 different scales were used to evaluate the depression level of the participators. A total of 25 studies were conducted in impatient setting and 28 studies were in outpatients setting; 32 used a certified music therapist, 15 not used a certified music therapist (for example researcher, nurse), and 10 not reported relevent information. A total of 16 different depression rating scales were used in the included studies, and HADS, GDS, and BDI were the most frequently used scales ( Table 1 ).

PRISMA diagram showing the different steps of systematic review, starting from literature search to study selection and exclusion. At each step, the reasons for exclusion are indicated. Doi: 10.1371/journal.pone.0052562.g001.

Note: BDI = Beck Depression Inventory; CDI = Children’s Depression Inventory; CDSS = depression scale for schizophrenia; CES-D = Center for Epidemiologic Studies Depression; CS = Cornell Scale; DMSRIA = Depression Mood Self-Report Inventory for Adolescence; EPDS = Edinburgh Postnatal Depression Scale; GDS-15 = Geriatric Depression Scale-15; GDS-30 = Geriatric Depression Scale-30; HADS = Hospital Anxiety and Depression Scale; HRSD (HAMD) = Hamilton Rating Scale for Depression; MADRS = Montgomery-sberg Depression Rating Scale; PROMIS = Patient Reported Outcomes Measurement Information System; SDS = Self-Rating Depression Scale; State-Trait Depression Questionnaire = ST/DEP; SV-POMS = short version of Profile of Mood States; NA = not available.

Of the 55 studies, only 2 studies had high risks of selection bias, and almost all of the included studies had high risks of performance bias ( Fig 2 ).

The overall effects of music therapy

Of the included 55 studies, 39 studies evaluated the music therapy, 17 evaluated the music medicine. Using a random-effects model, music therapy was associated with a significant reduction in depressive symptoms with a moderate-sized mean effect (SMD = −0.66; 95% CI = -0.86 to -0.46; P <0.001), with a high heterogeneity across studies ( I 2 = 83%, P <0.001); while, music medicine exhibited a stronger effect in reducing depressive symptom (SMD = −1.33; 95% CI = -1.96 to -0.70; P <0.001) ( Fig 3 ).

Twenty studies evaluated the active music therapy using a random-effects model, and a moderate-sized mean effect (SMD = −0.57; 95% CI = -0.90 to -0.25; P <0.001) was observed with a high heterogeneity across studies ( I 2 = 86.3%, P <0.001). Fourteen studies evaluated the receptive music therapy using a random-effects model, and a moderate-sized mean effect (SMD = −0.73; 95% CI = -1.01 to -0.44; P <0.001) was observed with a high heterogeneity across studies ( I 2 = 76.3%, P <0.001). Five studies evaluated the combined effect of active and receptive music therapy using a random-effects model, and a moderate-sized mean effect (SMD = −0.88; 95% CI = -1.32 to -0.44; P <0.001) was observed with a high heterogeneity across studies ( I 2 = 70.5%, P <0.001) ( Fig 4 ).

Among specific music therapy methods, recreative music therapy (SMD = -1.41; 95% CI = -2.63 to -0.20; P <0.001), guided imagery and music (SMD = -1.08; 95% CI = -1.72 to -0.43; P <0.001), music-assisted relaxation (SMD = -0.81; 95% CI = -1.24 to -0.38; P <0.001), music and imagery (SMD = -0.38; 95% CI = -0.81 to 0.06; P = 0.312), improvisational music therapy (SMD = -0.27; 95% CI = -0.49 to -0.05; P = 0.001), and music and discuss (SMD = -0.26; 95% CI = -1.12 to 0.60; P = 0.225) exhibited a different effect respectively ( Fig 5 ).

Sub-group analyses and meta-regression analyses

We performed sub-group analyses and meta-regression analyses to study the homogeneity. We found that music therapy yielded a superior effect on reducing depression in the studies with a small sample size (20–50), with a mean age of 50–65 years old, with medium intervention frequency (<3 times weekly), with more minutes per session (>60 minutes). We also found that music therapy exhibited a superior effect on reducing depression among people with severe mental disease /psychiatric disorder and depression compared with mental health people. While, whether the country have the music therapy profession, whether the study used group therapy or individual therapy, whether the study was in the outpatients setting or the inpatient setting, and whether the study used a certified music therapist all did not exhibit a remarkable different effect ( Table 2 ). Table 2 also presents the subgroup analysis of music medicine on reducing depression.

In the subgroup analysis by total session, music therapy and music medicine both exhibited a stronger effects of short (1–4 sessions) and medium length (5–12 sessions) compared with long intervention periods (>13sessions) ( Fig 6 ). Meta-regression demonstrated that total music intervention session was significantly associated with the homogeneity between studies ( P = 0.004) ( Table 3 ).

A, evaluating the effect of music therapy; B, evaluating the effect of music medicine.

Sensitivity analyses

We performed sensitivity analyses and found that re-estimating the pooled effects using fixed effect model, using trim and fill analysis, excluding the paper without information regarding music therapy, excluding the papers with more high biases, excluding the papers with small sample size (20< n<30), excluding the studies focused on the people with a severe mental disease, and excluding the papers using an infrequently used scale yielded the similar results, which indicated that the primary results was robust ( Table 4 ).

Evaluation of publication bias

We assessed publication bias using Egger’s linear regression test and funnel plot, and the results are presented in Fig 7 . For the main result, the observed asymmetry indicated that either the absence of papers with negative results or publication bias.

A, evaluating the publication bias of music therapy; B, evaluating the publication bias of music medicine; BDI = Beck Depression Inventory; CDI = Children’s Depression Inventory; CDSS = depression scale for schizophrenia; CES-D = Center for Epidemiologic Studies Depression; CS = Cornell Scale; DMSRIA = Depression Mood Self-Report Inventory for Adolescence; EPDS = Edinburgh Postnatal Depression Scale; GDS-15 = Geriatric Depression Scale-15; GDS-30 = Geriatric Depression Scale-30; HADS = Hospital Anxiety and Depression Scale; HRSD (HAMD) = Hamilton Rating Scale for Depression; MADRS = Montgomery-sberg Depression Rating Scale; PROMIS = Patient Reported Outcomes Measurement Information System; SDS = Self-Rating Depression Scale; State-Trait Depression Questionnaire = ST/DEP; SV-POMS = short version of Profile of Mood Stat.

Our present meta-analysis exhibited a different effect of music therapy and music medicine on reducing depression. Different music therapy methods also exhibited a different effect, and the recreative music therapy and guided imagery and music yielded a superior effect on reducing depression compared with other music therapy methods. Furthermore, music therapy and music medicine both exhibited a stronger effects of short and medium length compared with long intervention periods. The strength of this meta-analysis was the stable and high-quality result. Firstly, the sensitivity analyses performed in this meta-analysis yielded similar results, which indicated that the primary results were robust. Secondly, considering the insufficient statistical power of small sample size, we excluded studies with a very small sample size (n<20).

Some prior reviews have evaluated the effects of music therapy for reducing depression. These reviews found a significant effectiveness of music therapy on reducing depression among older adults with depressive symptoms, people with dementia, puerpera, and people with cancers [ 4 , 5 , 73 – 76 ]. However, these reviews did not differentiate music therapy from music medicine. Another paper reviewed the effectiveness of music interventions in treating depression. The authors included 26 studies and found a signifiant reduction in depression in the music intervention group compared with the control group. The authors made a clear distinction on the definition of music therapy and music medicine; however, they did not include all relevant data from the most recent trials and did not conduct a meta-analysis [ 77 ]. A recent meta-analysis compared the effects of music therapy and music medicine for reducing depression in people with cancer with seven RCTs; the authors found a moderately strong, positive impact of music intervention on depression, but found no difference between music therapy and music medicine [ 78 ]. However, our present meta-analysis exhibited a different effect of music therapy and music medicine on reducing depression, and the music medicine yielded a superior effect on reducing depression compared with music therapy. The different effect of music therapy and music medicine might be explained by the different participators, and nine studies used music therapy to reduce the depression among people with severe mental disease /psychiatric disorder, while no study used music medicine. Furthermore, the studies evaluating music therapy used more clinical diagnostic scale for depressive symptoms.

A meta-analysis by Li et al. [ 74 ] suggested that medium-term music therapy (6–12 weeks) was significantly associated with improved depression in people with dementia, but not short-term music therapy (3 or 4 weeks). On the contrary, our present meta-analysis found a stronger effect of short-term (1–4 weeks) and medium-term (5–12 weeks) music therapy on reducing depression compared with long-term (≥13 weeks) music therapy. Consistent with the prior meta-analysis by Li et al., no significant effect on depression was observed for the follow-up of one or three months after music therapy was completed in our present meta-analysis. Only five studies analyzed the therapeutic effect for the follow-up periods after music therapy intervention therapy was completed, and the rather limited sample size may have resulted in this insignificant difference. Therefore, whether the therapeutic effect was maintained in reducing depression when music therapy was discontinued should be explored in further studies. In our present meta-analysis, meta-regression results demonstrated that no variables (including period, frequency, method, populations, and so on) were significantly associated with the effect of music therapy. Because meta-regression does not provide sufficient statistical power to detect small associations, the non-significant results do not completely exclude the potential effects of the analyzed variables. Therefore, meta-regression results should be interpreted with caution.

Our meta-analysis has limitations. First, the included studies rarely used masked methodology due to the nature of music therapy, therefore the performance bias and the detection bias was common in music intervention study. Second, a total of 13 different scales were used to evaluate the depression level of the participators, which may account for the high heterogeneity among the trials. Third, more than half of those included studies had small sample sizes (<50), therefore the result should be explicated with caution.

Our present meta-analysis of 55 RCTs revealed a different effect of music therapy and music medicine, and different music therapy methods also exhibited a different effect. The results of subgroup analyses revealed that the characters of music therapy were associated with the therapeutic effect, for example specific music therapy methods, short and medium-term therapy, and therapy with more time per session may yield stronger therapeutic effect. Therefore, our present meta-analysis could provide suggestion for clinicians and policymakers to design therapeutic schedule of appropriate lengths to reduce depression.

Supporting information

S1 checklist, funding statement.

The Key Project of University Humanities and Social Science Research in Anhui Province (SK2017A0191) was granted by Education Department of Anhui Province; the Research Project of Anhui Province Social Science Innovation Development (2018XF155) was granted by Anhui Provincial Federation of Social Sciences; the Ministry of Education Humanities and Social Sciences Research Youth fund Project (17YJC840033) was granted by Ministry of Education of the People’s Republic of China. These funders had a role in study design, text editing, interpretation of results, decision to publish and preparation of the manuscript.

Data Availability

• PLoS One. 2020; 15(11): e0240862.

Decision Letter 0

PONE-D-20-17706

Effects of music therapy on depression: a meta-analysis of randomized controlled trials

Dear Dr. Ye,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

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Additional Editor Comments:

Dear Author,

Thank you for your valuable submission. I think it would be appropriate to emphasize the main problem first. Various musical interventions are used in medical settings to improve the patient's well-being, and of course, there are many publications on this subject. However, it is important to properly differentiate between these interventions for some important reasons I have pointed out below.

The music therapy definition you made, as "Music therapy was defined as music therapy provided by a qualified music teacher, psychological therapist, or nurse" is not universally accepted specific definition for music therapy. Moreover, the specific methods used in receptive music therapy include music-assisted relaxation, music and imagery, and Guided Imagery and Music (Bonny Method). Each of these may have different levels of effects on depression. It is not clear that which receptive music therapy studies in your review have used which of these methods. So, the majority of studies that you accepted as the receptive music therapy seems to be music medicine studies indeed. Similar critiques may also be apply to some of the studies you describe as active music therapy. Today, it is widely accepted that these music-based interventions should be divided into two major categories, namely music therapy (MT) and music medicine (MM). MM mainly based on patients' pre-recorded or rarely listening to live music and the direct effects of the music they listen to. In other words, MM aims to use music like medicines. It often managed by a medical professional other than a music therapist, and not needed a therapeutic relationship with the patients. Conversely, music therapy is the clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed music therapist who has completed an approved music therapy program. So, music therapy is a relational, interaction based form of therapy within a therapeutic relationship between the therapist and the client, and includes the triad of the music, the client and the music therapist. Since music therapy interventions is an evidence-based procedure using special music therapy methods of interventions and a more pragmatic approach than other music-based interventions, their effect levels and results are also different.

In the context of the above mentioned explanations, it is clear that to evaluate the effects of music therapy and other music based intervention studies together on depression can be misleading. The subjects I have mentioned so far have never been addressed in the introduction and discussion sections of your manuscript. I think that will be perceived as a major deficiency at least by the readers who are closer to the subject. In this sense, I think that an attentive revision considering the following views will be valuable and needed:

- The universally accepted definitions of music therapy (including active and receptive music therapy) and music medicine should be taken into account.

- It should be clarified that how many studies in your review did included a certified music therapist.

- Analyses, results and discussion should be submitted to the readers in accordance with all this distinctions and definitions. (The way to this seems to be to compare the effects of music medicine and music therapy on depression in parallel with the possible differences of music interventions used, and to discuss their possible implications on the results.)

- Another important point is that you did not mention nor discuss any of important reviews on same subject (for example please see: https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858. {"type":"entrez-nucleotide","attrs":{"text":"CD004517","term_id":"30321255","term_text":"CD004517"}} CD004517 .pub3/epdf/full or https://www.frontiersin.org/articles/10.3389/fpsyg.2017.01109/full or https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858. {"type":"entrez-nucleotide","attrs":{"text":"CD006911","term_id":"30323649","term_text":"CD006911"}} CD006911 .pub3/full)

I am aware that such a major revision will, in a sense, be a challenging way that may require a new analysis of your data. However, I believe you would appreciate that a study aimed at shedding light on potential music-based interventions in an important public health problem such as depression should not be misleading.

Thank you for your effort in advance.

Besides, according to the statistical reviewer who only reviewed the statistical approach used in this paper, there are two caveats:

1. The authors state that they excluded studies with fewer than 20 participants in one place in the paper (page 4), but fewer than 30 participants in another place in the paper (Table 4). This needs to be corrected for consistency.

2. The authors mention stronger effects of short and medium length vs. long music therapy periods in their results but there is no accompanying figure. I think it would be beneficial to show these findings in a figure (Forest plot).

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Research in Anhui Province (SK2017A0191), Research Project of Anhui Province Social Science

Innovation Development (2018XF155), Ministry of Education Humanities and Social Sciences

Research Youth fund Project (17YJC840033)."

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: No

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Reviewer #2: Yes

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Thank you for conducting this research and submitting it for publication consideration.

I recognize that English may not be the primary language of the authors. There are a few instances where the language could be improved, but that is mostly a copy-editing issue. There is also a lot of passive voice in the paper. I recommend making the voice active. This will enhance the readability of the paper.

I have a few comments that I hope will improve the paper.

1. Not all countries have an established music therapy profession. I recognize that this creates challenges for the authors! I'm wondering if the authors might consider including this as a factor in the analysis? For example, if a nurse provides "music therapy" in a country that does not have music therapy as a profession, is the effect equivalent as when a qualified music therapist in a country that has music therapy as a profession provides it? This might provide some incentive for occupational regulation and establishing professional music therapy associations.

2. please fix the "short title" (oxygen)

3. Music therapy with fewer minutes might yield superior effects. This may be misleading. Is there a minimum number of minutes? How many minutes might be optimal for therapeutic outcome? I believe it does make sense that longer sessions may result in less impact - quantity/duration does not always result in enhanced outcome.

4. I believe a stronger case needs to be made for the study. There are existing meta-analyses of MT for depression (Aalbers et al., 2017 Cochrane Review). What makes the current study unique and different? What are the gaps in the literature that warrant this study? Have there been a lot of recent additions to the literature that warrant a new meta-analysis?

5. A stronger discussion of the limitation of this study. Many studies did not evaluate a group with major depression/major depressive disorder (music therapy for chronic pain is important, but the variance of the populations under study does constitute a limitation). So, this study is not exclusive to adults with a major mental health condition. Might effects be different for people who are depressed versus people who are not depressed?

6. Instead of "blinding/blinded" please use "masking/masked."

7. Is there a citation that supports your classification of active versus receptive? (I would think Bruscia would be a good place to start with that...)

8. One item that I am not seeing is group therapy versus individual therapy. Did the authors screen for that? If so, is there an optimal group size? Are effects stronger when in a group format versus an individual format? This would have serious implications for clinical practice.

9. What about inpatient settings (such as a secure [locked] unit at a mental health facility) versus outpatient settings?

10. One item that I believe is missing is the dose. Not necessarily the duration (number of minutes) of each session, but the total number of sessions a participant has received. Gold has done some work in this area. Is there is a certain number of sessions that are needed to reach a therapeutic outcome? The number of sessions/week is good, but the number of total sessions is important.

11. Table 1 has the mean age. I recommend including the SD as well.

Thank you for taking the time to consider these suggestions. While receiving critical feedback can be difficult, please understand that my intentions are to improve the paper and ensure it has maximum impact. This is an important addition to the literature and I am grateful to the authors for their scholarship. I wish you the best!

Reviewer #2: This article addresses an important topic that is of interest to music therapists, psychiatrists and teachers and metal health practitioners. The statistics look promising. However, the major concern is that the definition of music therapy is theoretically and practically incorrect and misleading:

"7 Music therapy was defined as music therapy provided by a qualified music teacher, psychological

8 therapist, or nurse. " The study is missing several research studies that I am aware of and this makes its content suspicious. Also missing is a more depth-ful analysis of what active and passive music therapy is, and if it is indeed performed by those in other professions who have no training in 'musuc therapy;'-than the contents and findings are misleading and irrelevant.

Reviewer #3: I only reviewed the statistical approach used in this paper, which appeared appropriate for the research question under study. There are two caveats:

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Reviewer #1: No

Reviewer #3: No

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Author response to Decision Letter 0

29 Sep 2020

Response to Reviewers

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled " Effects of music therapy on depression: a meta-analysis of randomized controlled trials (PONE-D-20-17706)".

Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made revision which we hope meet with approval. All the revised portions were marked in red font in the new document. The main corrections in the paper and the responds to the reviewer’s comments are as flowing:

Response：We have studied comments carefully and revised the manuscript extensively according to the reviewer’s comments.

Firstly, We have amended the music therapy definition mainly based on the World Federation of Music Therapy (WFMT) and The American Music Therapy Association (AMTA), WFMT defines music therapy as “the professional use of music and its elements as an intervention inmedical, educational, and everyday environments with individuals, groups, families, or communities who seek to optimize their quality of life and improve their physical, social,communicative, emotional, intellectual, and spiritual health and wellbeing”. AMTA defines music therapy as “Music Therapy is the clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed professional who has completed an approved music therapy program”. [American Music Therapy Association (2020). Definition and Quotes about Music Therapy. Available online at: https://www.musictherapy.org/about/quotes/ (Accessed Sep 13, 2020).][van der Steen, J. T., et al. (2017). "Music-based therapeutic interventions for people with dementia." Cochrane Database Syst Rev 5: {"type":"entrez-nucleotide","attrs":{"text":"CD003477","term_id":"30320215","term_text":"CD003477"}} CD003477 .]

Secondly, we have re-studed all included papers carefully and added the specific intervention methods of each paper in table 1 (Table 1. Characteristics of clinical trials included in this meta-analysis). Two main types of music therapy were distinguished in our present study - receptive (or passive) and active music therapy. The specific methods used in receptive music therapy in our included papers including music-assisted relaxation, music and imagery, and guided imagery and music (Bonny Method), while the specific methods used in active music therapy included recreative music therapy, improvisational music therapy, song writing, and so on.

Thirdly, we have added some contents regarding the distinction between music therapy and music medicine in introduction and discussion sections of our manuscript.

The following contents are added in introduction section, “Today, it is widely accepted that the music-based interventions should be divided into two major categories, namely music therapy and music medicine. According to the American Music Therapy Association (AMTA), “music therapy is the clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed professional who has completed an approved music therapy program”. Therefore, music therapy is an established health profession in which music is used within a therapeutic relationship to address physical, emotional, cognitive, and social needs of individualst, and includes the triad of the music, the client and the qualified music therapist. [American Music Therapy Association (2020). Definition and Quotes about Music Therapy. Available online at: https://www.musictherapy.org/about/quotes/ (Accessed Sep 13, 2020).] While, music medicine is defined as mainly listening to prerecorded music provided by medical personnel or rarely listening to live music. In other words, music medicine aims to use music like medicines. It often managed by a medical professional other than a music therapist, and not needed a therapeutic relationship with the patients. Therefore, the essential difference of music therapy and music medicine is whether a therapeutic relationship is developed between a trained music therapist and the client.

[Bradt, J., et al. (2015). "The impact of music therapy versus music medicine on psychological outcomes and pain in cancer patients: a mixed methods study." Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer 23(5): 1261-1271.

[Yinger, O. S. and L. Gooding (2014). "Music therapy and music medicine for children and adolescents." Child and adolescent psychiatric clinics of North America 23(3): 535-553.]

【Tony Wigram．Inge Nyggard Pedersen＆Lars Ole Bonde，A Compmhensire Guide to Music Therapy．London and Philadelphia：Jessica Kingsley Publishen．2002：143．】

In the context of the clear distinction between these two major cagerories, it is clear that to evaluate the effects of music therapy and other music based intervention studies together on depression can be misleading. While, the distinction was not always clear in most of prior papers, and we found that no meta-analysis comparing the effects of music therapy and music medicine was conducted. Just a few studies made a comparison of music-based interventions on psychological outcomes between music therapy and music medicine. We aimed to (1) compare the effect between music therapy and music medicine on depression; (2) compare the effect between different specific methods used inmusic therapy on depression; (3) compare the effect of music-based interventions on depression among different population.

[Bradt, J., et al. (2015). "The impact of music therapy versus music medicine on psychological outcomes and pain in cancer patients: a mixed methods study." Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer 23(5): 1261-1271.[Yinger, O. S. and L. Gooding (2014). "Music therapy and music medicine for children and adolescents." Child and adolescent psychiatric clinics of North America 23(3): 535-553.]

The last, we have made a new analysis of our data. 1) including three new papers and re-analying of our data, 2) adding the comparison of music therapy and music medicine, 3) adding the comparison of impatient setting and outpatients setting, 4) adding the comparison of depressed people and not depressed people, 5）adding the comparison of countries have having music therapy profession and not, 6) adding the comparison of group therapy and individual therapy, 7) added the comparison of different intervention dose, and so on.

Response: (1)We have amended the of definitions of music therapy. The revised difinitons of music therapy was “Music Therapy is the clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed professional who has completed an approved music therapy program”. [American Music Therapy Association (2020). Definition and Quotes about Music Therapy. Available online at: https://www.musictherapy.org/about/quotes/ (Accessed Sep 13, 2020).]

We have added some contents on the distinction between music therapy (MT) and music medicine (MM) in introduction and discussion sections of our manuscript.

We have added the analysis of the comparion of music therapy (MT) and music medicine (MM) in Methord and Results sections

Response: we have re-studed all included papers carefully and added a new varible (Intervenor or therapist) into table 1, and the corresponding description was addded in the results section. Of 55 studies, 32 used a certified music therapist, 15 not used a certified music therapist (for example researcher, nurse), and 10 not reported relevent information.

Response: We have divided music-based interventions into two major categories, namely music therapy and music medicine according to the difinition. With respect to specific methods used in music therapy, we also have divided music therapy into receptive (or passive) and active music therapy. The specific methods used in receptive music therapy in our included papers including music-assisted relaxation, music and imagery, and guided imagery and music (Bonny Method), and the specific methods used in active music therapy included recreative music therapy and improvisational music therapy.

We have added some sub-group analyses by different music intervention categories, different music therapy categories, and specific music therapy methords.

The the above mentioned content have been added to Intruduction Analyses, results and discussion section.

Response: we are very sorry for not mentioning these important reviews. We have studied these reviews carefully and discussed these reviews in Discussion sections.

Some prior reviews have evaluated the effects of music therapy for reducing depression. Aalbers and colleagues included nine studies in their review; they concluded that music therapy provides short-term benefificial effects for people with depression, and suggested that high-quality trials with large sample size were needed. However, this review was limited to studies of individuals with a diagnosis of depression, and did not differentiate music therapy from music medicine. Another paper reviewed the effectiveness of music interventions in treating depression. The authors included 26 studies and found a signifiant reduction in depression in the music intervention group compared with the controp group. The authors made a clear distincition on the definition of music therapy and music medicine; however, they did not include all relevant data from the most recent trials and did not conduct a meta-analysis. A recent meta-analysis compared the effects of music therapy and music medicine for reducing depression in people with cancer with seven RCTs; the authors found a moderately strong, positive impact of music intervention on depression , but found no difference between music therapy and music medicine.

【Aalbers, S., et al. (2017). "Music therapy for depression." Cochrane Database Syst Rev 11: {"type":"entrez-nucleotide","attrs":{"text":"CD004517","term_id":"30321255","term_text":"CD004517"}} CD004517 .】

【Leubner, D. and T. Hinterberger (2017). "Reviewing the Effectiveness of Music Interventions in Treating Depression." Front Psychol 8: 1109.】

【Bradt, J., et al. (2016). "Music interventions for improving psychological and physical outcomes in cancer patients." Cochrane Database Syst Rev(8): {"type":"entrez-nucleotide","attrs":{"text":"CD006911","term_id":"30323649","term_text":"CD006911"}} CD006911 .】

To date, many new trials focued on music therapy and depression in differnt poupulation (such as people with cancer, people with dementia, people with chronic disease, and so on ) have been performed, but they have not yet been systematically reviewed.

Response: Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made revision according to the comments.

Response: We are sorry for making this mistake. In the Methord section, we defined exclusive criteria as studies with a very small sample size (n<20),while in table 4 we performed the sensitivity analyses by excluding the papers with smale sample size ( 20< n<30). We have amended the table 4.

Response: We have added these findings with a forest plot (figure 6) according to the comment.

 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

Response: We have amended our manuscript according to PLOS ONE's style requirements

Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files.

Response: We have adjusted these content according to the comment.

 3. Thank you for stating the following in the Acknowledgments Section of your manuscript:

Response: We would like to update our funding statement as follows: The funders had a role in study design, text editing, interpretation of results, decision to publish and preparation of the manuscript.

4.LOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

Response: We have created a new ORCID iD accordingly to your instructions.

Response: we are sorry for making this mistake, we have amended our list of authors on the manuscript accordingly.

Response: We have checked the refer to Figure 5 and found that the refer to figure 5 was a mistake, and we have amended it.

7. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information:  http://journals.plos.org/plosone/s/supporting-information .

 Response: we only have a Supporting Information files (PRISMA-2009-Checklist), and we have added the captions for this Supporting Information files accordingly. We also have updated in-text citations to match accordingly.

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Response: Thinks very much for your comment.

Response: Thinks very much for your comment. Our manuscript have been edited for proper English language, grammar, punctuation, spelling, and overall style by one qualified native English speaking editors.

Response: This suggestion is valuable and we have tried to judge if the countries in our inluded papers have an established music therapy profession by checking the author's work address, literature review, visiting the important website about music therapy, and consulting to some famous music therapist via emails. The following table showed that four countries may be not have a music therapy profession. We have added the comparison of the country having music therapy profession and not.

https://erikdalton.com/find-a-certified-therapist/

https://www.musictherapy.org/about/listserv/

Table 1. The information on the music therapy profession in the inluded papers

Country Country having music therapy profession

Korea Korean Music Therapy Association

South Korea Korean Music Therapy Association

UK British Association for Music Therapy

Australia Australian Music Therapy Association

Canada Canadian Association of Music Therapists

China Chinese Professional Music Therapist Association

Taiwan China Chinese Professional Music Therapist Association

Denmark Dansk forbund for musikterapie

Finland Finnish Society for Music Therapy

Hong Kong China Hong Kong Music Therapy and Counseling Association

Serbia Music Therapists of Serbia organize workshops

Switzerland Swiss Association of Music Therapy

USA The American Music Therapy Association

Singapore The Association for Music Therapy (Singapore)

Brazil Uniao Braileira Das Associacoes De Musicoterapia

Germany YES

Northern Ireland YES

Spanish YES

Venezuela No

2.please fix the "short title" (oxygen)

Response: We’re sorry for making this mistake, and we have corrected this mistake.

Music therapy with fewer minutes might yield superior effects. This may be misleading. Is there a minimum number of minutes? How many minutes might be optimal for therapeutic outcome? I believe it does make sense that longer sessions may result in less impact - quantity/duration does not always result in enhanced outcome.

Response: In 33 included trials, intervention time each session was different, the mimimum time was 15 minutes in only one study (Burrai et al., 2019b), followed by 20 minuters in four studies (Chirico et al., 2020; Guétin et al., 2009; Hanser et al., 1994; Sigurdardóttir et al., 2019). In our subgroup analysis by time per session (minutes), we divided time per session into three groups, namely 15-40, 41-60, >60, and this presentation might be unclear.

In order to respond this comment, we have re-divided the time per session into four groups, namely 15-40, 41-60, 61-120, to explore the optimal minuter per session for therapeutic outcome.

I believe a stronger case needs to be made for the study. There are existing meta-analyses of MT for depression (Aalbers et al., 2017 Cochrane Review). What makes the current study unique and different? What are the gaps in the literature that warrant this study? Have there been a lot of recent additions to the literature that warrant a new meta-analysis?

Response: Some prior reviews have evaluated the effects of music therapy for reducing depression. Aalbers and colleagues (Aalbers et al., 2017）included nine studies in their review; they concluded that music therapy provides short-term benefificial effects for people with depression, and suggested that high-quality trials with large sample size were needed. However, this review was limited to studies of individuals with a diagnosis of depression, and did not differentiate music therapy from music medicine.

Another paper reviewed the effectiveness of music interventions in treating depression. The authors (Leubner D., 2017) included 26 studies and found a signifiant reduction in depression in the music intervention group compared with the controp group. The authors made a clear distincition on the definition of music therapy and music medicine; however, they did not include all relevant data from the most recent trials and did not conduct a meta-analysis. A recent meta-analysis (Bradt et al., 2016) compared the effects of music therapy and music medicine for reducing depression with seven RCTs; the authors found a moderately strong, positive impact of music intervention on depression , but found no difference between music therapy and music medicine. However, this review was limited to studies of individuals with a diagnosis of cancer.

Figure 1 presents the number of published paper ( search from Pubmed) focued on music therapy and depression from 1983 to 2020, the published paper was in the rapidly growing stage during the past five years. While, the above mentioned reviews all included papers published before 2017. To date, many new trials focued on music therapy and depression in differnt poupulation (such as people with cancer, people with dementia, people with chronic disease, and so on ) have been performed, but they have not yet been systematically reviewed.

While, no meta-analysis compared the the difference of music therapy on depression in differnt poupulation (such as people with depression, people with dementia, people with chronic disease, health people, and so on ) have been performed.

Figure 1 The pubished papers from 1983 to 2020 focused on music therapy and depression (searched from Pubmed)

In our persent meta-analysis, we aimed to (1) compare the effect between music therapy and music medicine on depression; (2) compare the effect between different specific methods used inmusic therapy on depression; (3) compare the effect of music-based interventions on depression among different population.

We have added the above content to Intruduction and Dissussion sections.

5.A stronger discussion of the limitation of this study. Many studies did not evaluate a group with major depression/major depressive disorder (music therapy for chronic pain is important, but the variance of the populations under study does constitute a limitation). So, this study is not exclusive to adults with a major mental health condition. Might effects be different for people who are depressed versus people who are not depressed?

Response: This is a very important comment. According to this comment, we have made some revision.

Firstly, we have added a sensitivity analysis by excluding the studes focused on the people with a major mental health condition.

Secondly, we have re-grouped the populations into three groups, namely mental health, severe mental disease /psychiatric disorder, and depression and we have added the subgroup analysis (table 2 in revised manuscript)..

Thirdly, we have added the analysis of the difference between people who are depressed versus people who are not depressed accordingly (table 2 in revised manuscript).

6.Instead of "blinding/blinded" please use "masking/masked."

Response: We have replaced "blinding/blinded" with "masking/masked" according to this comment.

Response: In active methods (improvisational, re-creative, compositional), participants are ‘making music’ , and in receptive music therapy (music-assisted relaxation, music and imagery, guided imagery and music, lyrics analysis ), participants are ‘receiving’ (e.g. listening to) music (Bruscia 2014; Wheeler 2015).

We have amended the difinition and added the citation to the Result section according to this commment.

[Bruscia KE. Defining Music Therapy. 3rd Edition.University Park, Illinois, USA: Barcelona Publishers, 2014.]

[Wheeler BL. Music Therapy Handbook. New York, New York, USA: Guilford Publications, 2015.]

Response: Of the 55 studies, 38 used group therapy, 17 used individual therapy, and 2 not reported. We have added the comparison of group therapy versus individual therapy according to this comment (table 2 in revised manuscript).

Response: Of 55 studies, a total of 25 studies were conducted in impatient setting,28 studies were in outpatients setting setting, and 2 studies not repoted the setting. We have added the subgroup analysis by inpatient settings (secure [locked] unit at a mental health facility versus outpatient settings) according to this comment (table 2 in revised manuscript).

Response: We have added the subgroup analysis by total sessions a participant has received according to this comment.

Response: We have added the SD in table 1

Response: Thanks very much for your important comments, these comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches.

Response: (1) We have amendded the difinition of music therapy. According to the American Music Therapy Association (AMTA), “music therapy is the clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed professional who has completed an approved music therapy program”.. [American Music Therapy Association (2020). Definition and Quotes about Music Therapy. Available online at: https://www.musictherapy.org/about/quotes/ (Accessed Sep 13, 2020).]

(2)We are very sorry for missing several research studies in our present meta-analysis. According to this comment, we have performed more extensive electronic search using the following terms: depression or mood disorders or affective disorders and music. We also performed manual search for the reference of all relevent reviews. In order to ensure the study quality of included papers, we excluded the studies with a very small sample size (n<20), we also excluded the non-english papers due to our language barrier. We included 23 new papers and deleted 1 old paper, in the last a total of 55 paper were included in our present analysis. The following are the new included papers and some excluded papers:

New-included papers

1)Albornoz Y. The effects of group improvisational music therapy on depression in adolescents and adults with substance abuse: a randomised controlled trial. Nordic Journal of Music Therapy 2011;20(3):208–24.

2)Hendricks CB, Robinson B, Bradley B, Davis K. Using music techniques to treat adolescent depression. Journal of Humanistic Counseling, Education and Development 1999; 38:39–46. （unavaliable）

3)Hendricks CB. A study of the use of music therapy techniques in a group for the treatment of adolescent depression. Dissertation Abstracts International 2001;62(2-A):472.

4)Radulovic R. The using of music therapy in treatment of depressive disorders. Summary of Master Thesis. Belgrade: Faculty of Medicine University of Belgrade, 1996.

5)Zerhusen JD, Boyle K, Wilson W. Out of the darkness: group cognitive therapy for depressed elderly. Journal of Military Nursing Research 1995;1:28–32. PUBMED: 1941727]

6)Chen SC, Yeh ML, Chang HJ, Lin MF. Music, heart rate variability, and symptom clusters: a comparative study. Support Care Cancer. 2020;28(1):351-360. doi:10.1007/s00520-019-04817-x

7)Chang, M. Y., Chen, C. H., and Huang, K. F. (2008). Effects of music therapy on psychological health of women during pregnancy. J. Clin. Nurs. 17, 2580–2587. doi: 10.1111/j.1365-2702.2007.02064.x

8)Chen XJ, Hannibal N, Gold C. Randomized Trial of Group Music Therapy With Chinese Prisoners: Impact on Anxiety, Depression, and Self-Esteem. Int J Offender Ther Comp Criminol. 2016;60(9):1064-1081. doi:10.1177/0306624X15572795

9)Esfandiari, N., and Mansouri, S. (2014). The effect of listening to light and heavy music on reducing the symptoms of depression among female students. Arts Psychother. 41, 211–213. doi: 0.1016/j.aip.2014.02.001

10)Fancourt, D., Perkins, R., Ascenso, S., Carvalho, L. A., Steptoe, A., and Williamon, A. (2016). Effects of group drumming interventions on anxiety, depression, social resilience and inflammatory immune response among mental health service users. PLoS ONE 11:e0151136. doi: 10.1371/journal.pone.0151136

11)Giovagnoli AR, Manfredi V, Parente A, Schifano L, Oliveri S, Avanzini G. Cognitive training in Alzheimer's disease: a controlled randomized study. Neurol Sci. 2017;38(8):1485-1493. doi:10.1007/s10072-017-3003-9

12)Harmat, L., Takács, J., and Bodizs, R. (2008). Music improves sleep quality in students. J. Adv. Nurs. 62, 327–335. doi: 10.1111/j.1365-2648.2008.04602.x

13)Liao J, Wu Y, Zhao Y, et al. Progressive Muscle Relaxation Combined with Chinese Medicine Five-Element Music on Depression for Cancer Patients: A Randomized Controlled Trial. Chin J Integr Med. 2018;24(5):343-347. doi:10.1007/s11655-017-2956-0

14)Lu, S. F., Lo, C. H. K., Sung, H. C., Hsieh, T. C., Yu, S. C., and Chang, S. C. (2013). Effects of group music intervention on psychiatric symptoms and depression in patient with schizophrenia. Complement. Ther. Med. 21, 682–688. doi: 10.1016/j.ctim.2013.09.002

15)Mahendran R, Gandhi M, Moorakonda RB, et al. Art therapy is associated with sustained improvement in cognitive function in the elderly with mild neurocognitive disorder: findings from a pilot randomized controlled trial for art therapy and music reminiscence activity versus usual care. Trials. 2018;19(1):615. Published 2018 Nov 9. doi:10.1186/s13063-018-2988-6

16)Nwebube C, Glover V, Stewart L. Prenatal listening to songs composed for pregnancy and symptoms of anxiety and depression: a pilot study. BMC Complement Altern Med. 2017;17(1):256. Published 2017 May 8. doi:10.1186/s12906-017-1759-3

17)Porter S, McConnell T, McLaughlin K, et al. Music therapy for children and adolescents with behavioural and emotional problems: a randomised controlled trial. J Child Psychol Psychiatry. 2017;58(5):586-594. doi:10.1111/jcpp.12656

18)Raglio A, Giovanazzi E, Pain D, et al. Active music therapy approach in amyotrophic lateral sclerosis: a randomized-controlled trial. Int J Rehabil Res. 2016;39(4):365-367. doi:10.1097/MRR.0000000000000187

19)Torres E, Pedersen IN, Pérez-Fernández JI. Randomized Trial of a Group Music and Imagery Method (GrpMI) for Women with Fibromyalgia. J Music Ther. 2018;55(2):186-220. doi:10.1093/jmt/thy005

20)Verrusio, W., Andreozzi, P., Marigliano, B., Renzi, A., Gianturco, V., Pecci, M. T., et al. (2014). Exercise training and music therapy in elderly with depressive syndrome: a pilot study. Complement. Ther. Med. 22, 614–620. doi: 10.1016/j.ctim.2014.05.012

21)Wang, J. , Wang, H. and Zhang, D. (2011) Impact of group music therapy on the depression mood of college students. Health, 3, 151-155

22)Yap AF, Kwan YH, Tan CS, Ibrahim S, Ang SB. Rhythm-centred music making in community living elderly: a randomized pilot study. BMC Complement Altern Med. 2017 Jun 14;17(1):311. doi: 10.1186/s12906-017-1825-x. PMID: 28615007; PMCID: PMC5470187.

23)Koelsch, S., Offermanns, K., and Franzke, P. (2010). Music in the treatment of affective disorders: an exploratory investigation of a new method for music-therapeutic research. Music Percept. Interdisc. J. 27, 307–316. doi: 10.1525/mp.2010.27.4.307

Excluded papers：

24)Bally, K., Campbell, D., Chesnick, K., and Tranmer, J. E. (2003). Effects of patient controlled music therapy during coronary angiography on procedural pain and anxiety distress syndrome. Crit. Care Nurse 23, 50–58. （not provide useable data）

25)Atiwannapat P, Thaipisuttikul P, Poopityastaporn P, Katekaew W. Active versus receptive group music therapy for major depressive disorder - a pilot study. Complementary Therapies in Medicine 2016;26:141–5. (sample size<20)

26)Garrido S, Stevens CJ, Chang E, Dunne L, Perz J. Music and Dementia: Individual Differences in Response to Personalized Playlists. J Alzheimers Dis. 2018;64(3):933-941. doi:10.3233/JAD-180084 （not randomised or quasi-randomised controlled trials）

27)Sánchez A, Maseda A, Marante-Moar MP, de Labra C, Lorenzo-López L, Millán-Calenti JC. Comparing the Effects of Multisensory Stimulation and Individualized Music Sessions on Elderly People with Severe Dementia: A Randomized Controlled Trial. J Alzheimers Dis. 2016;52(1):303-315. doi:10.3233/JAD-151150 （the control group also received music intervention）

28)Mondanaro JF, Homel P, Lonner B, Shepp J, Lichtensztein M, Loewy JV. Music Therapy Increases Comfort and Reduces Pain in Patients Recovering From Spine Surgery. Am J Orthop (Belle Mead NJ). 2017;46(1):E13-E22. (No full text available)

29)Castillo-Pérez, S., Gómez-Pérez, V., Velasco, M. C., Pérez-Campos, E., and Mayoral, M. A. (2010). Effects of music therapy on depression compared with psychotherapy. Arts Psychother. 37, 387–390. doi: 0.1016/j.aip.2010.07.001 （not provide useable data)

30)Alcântara-Silva TR, de Freitas-Junior R, Freitas NMA, et al. Music Therapy Reduces Radiotherapy-Induced Fatigue in Patients With Breast or Gynecological Cancer: A Randomized Trial. Integr Cancer Ther. 2018;17(3):628-635. doi:10.1177/1534735418757349（not provide useable data)

31)Cheung CWC, Yee AWW, Chan PS, et al. The impact of music therapy on pain and stress reduction during oocyte retrieval - a randomized controlled trial. Reprod Biomed Online. 2018;37(2):145-152. doi:10.1016/j.rbmo.2018.04.049（not provide useable data)

32)Pezzin LE, Larson ER, Lorber W, McGinley EL, Dillingham TR. Music-instruction intervention for treatment of post-traumatic stress disorder: a randomized pilot study. BMC Psychol. 2018;6(1):60. Published 2018 Dec 19. doi:10.1186/s40359-018-0274-8 (the control group also received music intervention)

33)Silverman, M. J. (2011). Effects of music therapy on change and depression on clients in detoxification. J. Addict. Nurs. 22, 185–192. doi: 10.3109/10884602.2011.616606 （the control group also received music intervention）

34)Särkämö T, Laitinen S, Numminen A, Kurki M, Johnson JK, Rantanen P. Clinical and Demographic Factors Associated with the Cognitive and Emotional Efficacy of Regular Musical Activities in Dementia. J Alzheimers Dis. 2016;49(3):767-81. doi: 10.3233/JAD-150453. PMID: 26519435.

35)Tuinmann G, Preissler P, Böhmer H, Suling A, Bokemeyer C. The effects of music therapy in patients with high-dose chemotherapy and stem cell support: a randomized pilot study. Psychooncology. 2017 Mar;26(3):377-384. doi: 10.1002/pon.4142. Epub 2016 May 5. PMID: 27146798.（not provide useable data)

36)Hsu, W. C., and Lai, H. L. (2004). Effects of music on major depression in psychiatric inpatients. Arch. Psychiat. Nurs. 18, 193–199. doi: 10.1016/j.apnu.2004.07.007（not provide useable data)

（3）We have added some new analyses of our data. 1) including three new papers and re-analying of our data, 2) adding the comparison of music therapy and music medicine (figure 3 in revised manuscript) , 3) adding some subgroup analyses by country having music therapy profession, intervention settings, therapy mode, specific music therapy methord, intervenor /therapist, and total intervention session (table 2 in revised manuscript) .

Response: We are sorry for making this mistake. In the Methord section, we defined exclusive criteria as studies with a very small sample size (n<20),while in table4 we performed the sensitivity analyses by excluding the papers with smale sample size ( 20< n<30). We have amended the table 4.

Response: We have added these findings with a forest plot (figure 6 in revised manuscript) according to the comment.

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Existing approaches to decoding music from brain signals include using fMRI and ECoG, which, while effective, involve either impractical real-time application or invasive techniques. Non-invasive EEG methods have been explored, but they often require manual data preprocessing and focus on simpler auditory stimuli. For instance, previous studies have successfully decoded music from EEG signals. Still, these methods were limited to simpler, monophonic tunes and required extensive data cleaning and manual channel selection.

Researchers from Ca’ Foscari University of Venice, Sapienza University of Rome, and Sony CSL have introduced a novel method using latent diffusion models to decode naturalistic music from EEG data. This approach aims to improve the quality and complexity of the decoded music without extensive data preprocessing. By leveraging ControlNet, a parameter-efficient fine-tuning method for diffusion models, the researchers conditioned a pre-trained diffusion model on raw EEG signals. This innovative approach seeks to overcome the limitations of previous methods by handling complex, polyphonic music and reducing the need for manual data handling.

The proposed method employs ControlNet to condition a pre-trained diffusion model on raw EEG signals. ControlNet integrates EEG data with the diffusion model to generate high-quality music by mapping brainwave patterns to complex auditory outputs. The architecture uses minimal preprocessing, such as a robust scaler and standard deviation clamping, to ensure data integrity without extensive manual intervention. The EEG signals are mapped to latent representations via a convolutional encoder, which is then used to guide the diffusion process, ultimately producing naturalistic music tracks. This method also incorporates neural embedding-based metrics for evaluation, providing a robust framework for assessing the quality of the generated music.

The performance of the new method was evaluated using various neural embedding-based metrics. The research demonstrated that their model significantly outperformed traditional convolutional networks in generating more accurate musical reconstructions from EEG data. For instance, the ControlNet-2 model achieved a CLAP Score of 0.60, while the baseline convolutional network scored significantly lower. Regarding the Frechet Audio Distance (FAD), the proposed method achieved a score of 0.36, indicating high-quality generation, compared to 1.09 for the baseline. Furthermore, the mean square error (MSE) was reduced to 148.59 in the proposed method, highlighting its superior performance in reconstructing detailed musical characteristics from EEG data. The Pearson Coefficient also reflected improved accuracy, with the ControlNet-2 model achieving a correlation coefficient of 0.018, indicating a closer match between the generated and ground truth tracks.

In conclusion, the research addresses the challenge of decoding complex music from non-invasive brain signals by introducing a novel, minimally invasive method. The proposed diffusion model shows promising results in accurately reconstructing naturalistic music, marking a significant advancement in brain-computer interfaces and auditory decoding. The method’s ability to handle complex, polyphonic music without extensive manual preprocessing sets a new benchmark in EEG-based music reconstruction, paving the way for future developments in non-invasive BCIs and their applications in various domains.

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Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.

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Get science-backed answers as you write with Paperpal's Research feature

How to Ace Grant Writing for Research Funding with Paperpal 

The National Institutes of Health (NIH)—a major federal funding agency for scientific research—reported a 20% success rate 1 for funding of new research projects in 2022. That is, only one in 10 applicants who submitted grant applications from across the globe received monetary support from the NIH to conduct their research.  

As a researcher, grappling with the tedious and intensive process of grant writing and making these numbers work in your favor is crucial to your career, no matter where you are in your trajectory of climbing up the academic ladder. 

Let’s put things in perspective first—what is it about grant applications that makes them so elusive to begin with, and what strategies and best practices can you align with to put your best foot forward in writing a grant? To answer these questions, it is imperative to understand what reviewers look for in grant applications and where most of these applications falter. 

Table of Contents

• Grant Writing: What Reviewers Look for in Research Funding Applications 

Align well with the grant requirements

Pin down the ‘so what’ of your proposal, get your grant application critiqued.

• How to Write Strong Grant Applications with Paperpal  

Grant Writing: What Reviewers Look for in Research Funding Applications

According to grant reviewers at the National Institute of Allergy and Infectious Diseases (NIAID), 2 many applicants fall short in failing to support their hypotheses and discussing contingency plans in case their original approach does not work, while others are weak in making a case for why the studies are being done in the first place. Although a number of factors contribute to whether your application makes the cut or not, in essence, reviewers place irrevocable emphasis on the scientific merit of the grant application when making research funding decisions. This can be preliminarily judged on the following criteria: 

• Influence in the field of research: If the project’s aims are achieved, how would the current scientific knowledge and capability advance and further drive the field forward? 
• Challenging the status quo: Does the proposed research seek to move the needle on the currently accepted concepts and approaches? 
• Well-reasoned approach: Is the project founded on strategies that ensure an unbiased inquiry, with a sound understanding of potential challenges, alternatives, and benchmarks/milestones of the project? 

Although not an exhaustive list, these factors are foundational to what makes a well-rounded grant application with higher chances of being favorably reviewed. A case in point is this publicly available grant application by Dr. David Wagner, a recognized voice in the field of autoimmune research, particularly type 1 diabetes. 3  The grant reviewers lauded the applicant for setting a strong premise for the proposed study based on reliable published data, rigor of the experimental approach, and backing the application with promising preliminary results.  

3 Practical Grant Writing Tips to Elevate Your Grant Applications

Creating the perfect grant application and navigating the grant writing process can be daunting. You need to do your research and get science-backed insights to support your proposal. Here are some go-to tips to polish your grant application and put your best foot forward: 

Read the submission criteria and driving principles of the grant organization thoroughly and ensure that you highlight how your proposal befits them. What may seem to be a simple housekeeping step can go a long way to convince reviewers that you have done the due diligence of tailoring your grant application to the requirements and expectations of the funding body.  

While drafting your grant application, focus on why you want to investigate the aims that you identify and why the outcome of your research would be of importance in a larger and broader context. Developing a sharp elevator pitch can help you get closer to a convincing answer of the ‘so what’ of your proposal. 

A thorough critique early on in the grant writing journey by friends, colleagues, or professional grant reviewers/editing services is critical. This can help you break out of your thought patterns and biases and reflect on your proposal from multiple facets and perspectives. The diversity and depth that this exercise can bring to your proposal would be a great value addition in the favor of your grant application. 

How to Write Strong Grant Applications with Paperpal

The art of competitive grant writing involves writing about facts and ideas with intrigue and from a broader point of view, something that may not be second nature to an academic who is already juggling with a desk load of tasks from troubleshooting experiments, drafting rebuttals for their manuscripts’ peer reviews, and attending to administrative duties.  

With Paperpal as your academic writing guide, you can create a compelling narrative for your grant application. A well-rounded understanding of scientific literature is crucial in backing and validating your ideas that are still to be put to the test in a laboratory. With Paperpal’s ‘Research’ feature, you can get answers to specific research enquiries from a repository of over 250 million research publications. Using these inputs, you can back up your hypothesis with relevant findings. This would give you a good footing to set a strong premise for your grant application. 

Here’s a quick 3-step process to leverage Paperpal’s Research feature effectively:  

1. Submit your query: Login or sign-up to Paperpal for free and click on the Research tab. Type your question in simple English to get instant factual insights from over 250 million articles, with verified sources.  

2. Review and save: Scan the summarized response and citations provided by the AI; click to read related references and save relevant papers to your citation library.   

3. Cite references: Use these insights to develop your grant writing; don’t forget to cite relevant references to deliver a well-thought-out grant application for research funding. 

Paperpal transforms how you research and write, turning a typically long, disjointed process into a seamless experience. Try Paperpal for free today to back your ideas with evidence and nail the grant writing game!    References:  

• Lauer, M. FY 2022 By the Numbers: Extramural Grant Investments in Research. National Institutes of Health, March 2023. Available online at https://nexus.od.nih.gov/all/2023/03/01/fy-2022-by-the-numbers-extramural-grant-investments-in-research/  
• Mohan-Ram, V. Murder Most Foul: How Not to Kill a Grant Application. Science, January 2000. Available online at https://www.science.org/content/article/murder-most-foul-how-not-kill-grant-application  
• Wagner, D.H. Developing a small peptide to control autoimmune inflammation in type 1 diabetes. Available online at https://www.niaid.nih.gov/sites/default/files/R41-Wagner-Summary-Statement.pdf  

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• Research Outlines: How to Write An Introduction Section in Minutes with Paperpal Copilot
• Addressing Your Queries on AI Ethics, Plagiarism, and AI Detection
• Paperpal’s New AI Research Finder Empowers Authors to Research, Write, Cite, All in One Place
• How Paperpal is Enhancing Academic Productivity and Accelerating Research in China

How to Write a High-Quality Conference Paper

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Anusandhan 2.0: IIT Mandi seeks applications for research and innovation fair

The indian institute of technology, mandi is inviting researchers and scholars to apply for anusandhan 2.0, a comprehensive multidisciplinary research and innovation fair scheduled for june 18th and 19th, 2024..

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• The last date for abstract submission is June 5, 2024
• Anusandhan 2.0 anticipates featuring over 20 distinguished speakers
• Authors of accepted papers must submit their full-length camera-ready versions by June 15

The Indian Institute of Technology, IIT Mandi is inviting applications from researchers and scholars for a comprehensive multidisciplinary research and innovation fair, Anusandhan 2.0, scheduled for June 18 and 19, 2024. During this two-day event, eminent scholars, researchers, and industry experts from all IITs will gather to present and discuss pioneering ideas and innovations.

Researchers and scholars are invited to participate in paper presentations, poster proposals and interactive sessions under the theme Interdisciplinary Research: Goals for a Sustainable Future. Interested participants can submit their abstracts or full-length papers using the link: https://scri.iitmandi.ac.in/anusandhan-main/home.html

The last date for abstract submission is June 5, 2024, with the final registration deadline set for June 7, 2024. Authors of accepted papers must submit their full-length camera-ready versions by June 15, 2024.

• Engineering & Technology (Mechanical Engineering, Civil Engineering, Computing and Electrical Engineering, Electronics Engineering, Robotics, IKSMHA, Computer Science, and AI&ML)
• Basic Sciences (Physics, Chemistry, Mathematics, Biology, and Environmental Sciences)
• Medical Science (Healthcare, Mental Health, Physiology, and Medicine)
• Management and Humanities & Social Sciences (Economics, Literature, History, Sociology, Translation Studies, and Psychology)

Expressing his enthusiasm for the event, Dr Hitesh Shrimali, Dean of Students at IIT Mandi, said, that Anusandhan 2.0, an annual research fair, would be an exciting platform to spark the technical creativity of the nation's youth. Considering the broad theme of the fair, it will foster cross-disciplinary collaborations, bring new ideas and serve as a testament to our potential to address complex societal challenges by Indian research scholars.

General Chair- Anusandhan 2.0, Dr G Srikanth Reddy, said that Anusandhan 2.0 brings in great diversity by including a wide spectrum of research areas which include technology, science and society. With multidisciplinary themes, this conference possesses huge potential towards truly reflecting technology for society's goals.

• Professor Ramancharla Pradeep Kumar, Director, CSIR-CBRI
• Professor Rama Shankar Verma, Director, MNNIT Allahabad, Formerly Professor, IIT Madras
• Professor N P Padhy, Director, MNIT Jaipur, Formerly Professor, IIT Roorkee
• Professor Animesh Biswas, Professor, IIT Kanpur, Ex-Director, NIT Rourkela
• Professor Arup Kumar Das, Department of Mechanical and Industrial Engineering, IIT Roorkee

In addition to the keynote speakers, experts from the industry and multiple alumni of IIT Mandi will hold talks and discussion sessions during the 2-day event.

The event will primarily concentrate on three thematic areas with multiple sub-themes encompassing key aspects of Engineering & Technologies, Fundamental Sciences, and Humanities & Social Sciences and Management. Exceptional presentations and exhibitions from each field and sub-theme will be recognised, nurturing a spirit of competitiveness and innovation.

In the end, a total of 18 prizes will be offered to the winners, including Rs. 44,000 and 18 coupons for purchasing books from Springer Publications. The top 3 prizes will be awarded for both PowerPoint and poster presentations. In addition to this, all participants will receive certificates.

Anusandhan 2.0 is dedicated to celebrating the vibrant research atmosphere and scholastic engagements at IIT Mandi. The fair aims to promote Himalayan engagement with research initiatives and hill-based events, attracting researchers from diverse fields such as Science, Humanities and Social Sciences, Engineering, and Management.

This event is organised by The SCRI club of Research Society Students Gymkhana, IIT Mandi.