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Music listening has experienced a sharp increase during the last decade thanks to music streaming and recommendation services. While they offer text-based search functionality and provide recommendation lists of remarkable utility, their typical mode of interaction is unidimensional, i.e., they provide lists of consecutive tracks, which are commonly inspected in sequential order by the user. The user experience with such systems is heavily affected by cognition biases (e.g., position bias, human tendency to pay more attention to first positions of ordered lists) as well as algorithmic biases (e.g., popularity bias, the tendency of recommender systems to overrepresent popular items). This may cause dissatisfaction among the users by disabling them to find novel music to enjoy. In light of such systems and biases, we propose an intelligent audiovisual music exploration system named EMOMTB . It allows the user to browse the entirety of a given collection in a free nonlinear fashion. The navigation is assisted by a set of personalized emotion-aware recommendations, which serve as starting points for the exploration experience. EMOMTB adopts the metaphor of a city, in which each track (visualized as a colored cube) represents one floor of a building. Highly similar tracks are located in the same building; moderately similar ones form neighborhoods that mostly correspond to genres. Tracks situated between distinct neighborhoods create a gradual transition between genres. Users can navigate this music city using their smartphones as control devices. They can explore districts of well-known music or decide to leave their comfort zone. In addition, EMOMTB integrates an emotion-aware music recommendation system that re-ranks the list of suggested starting points for exploration according to the user’s self-identified emotion or the collective emotion expressed in EMOMTB ’s Twitter channel. Evaluation of EMOMTB has been carried out in a threefold way: by quantifying the homogeneity of the clustering underlying the construction of the city, by measuring the accuracy of the emotion predictor, and by carrying out a web-based survey composed of open questions to obtain qualitative feedback from users. Fonds zur Förderung der Wissenschaftlichen Forschung P33526 and P36413 Version of record

Music streaming platforms allow users to enjoy music from all over the globe.Such opportunity speeds up cultural exchange between different countries, a process often associated with globalization. While such an exchange could lead to more diverse music consumption, empirical evidence on its influence on online music consumption is limited. Besides, the extent to which music recommender systems foster exchange or amplify globalization in music remains an understudied problem.In this paper, we present findings from an empirical study to detect traces of globalization in domestic vs. foreign online music consumption. Besides, we investigate if popular recommendation algorithms, specifically ItemKNN and NeuMF, are prone to amplifying globalization processes. Our experiments on Last.fm listening data show nuanced patterns of globalization in music consumption. We observe a strong position of US music in all considered countries. In countries such as Sweden, Great Britain, or Brazil, US music shows various levels of coexistence with domestic music. We find that Finland is least influenced by US music, while greatly consuming and 'exporting' domestic music. With respect to recommendation algorithms, ItemKNN tends to recommend domestic music to users of many countries, while NeuMF contributes to accelerating globalization and shifting balance towards dominance of US music on the market.

Musical listening is broadly used as an inexpensive and safe method to reduce self-perceived anxiety. This strategy is based on the emotivist assumption claiming that emotions are not only recognised in music but induced by it. Yet, the acoustic properties of musical work capable of reducing anxiety are still under-researched. To fill this gap, we explore whether the acoustic parameters relevant in music emotion recognition are also suitable to identify music with relaxing properties. As an anxiety indicator, the positive statements from the six-item Spielberger State-Trait Anxiety Inventory, a self-reported score from 3 to 12, are taken. A user-study with 50 participants assessing the relaxing potential of four musical pieces was conducted; subsequently, the acoustic parameters were evaluated. Our study shows that when using classical Western music to reduce self-perceived anxiety, tonal music should be considered. In addition, it also indicates that harmonicity is a suitable indicator of relaxing music, while the role of scoring and dynamics in reducing non-pathological listener distress should be further investigated.

Musical listening is broadly used as an inexpensive and safe method to reduce self-perceived anxiety. This strategy is based on the emotivist assumption claiming that emotions are not only recognised in music but induced by it. Yet, the acoustic properties of musical work capable of reducing anxiety are still under-researched. To fill this gap, we explore whether the acoustic parameters relevant in music emotion recognition are also suitable to identify music with relaxing properties. As an anxiety indicator, the positive statements from the six-item Spielberger State-Trait Anxiety Inventory, a self-reported score from 3 to 12, are taken. A user-study with 50 participants assessing the relaxing potential of four musical pieces was conducted; subsequently, the acoustic parameters were evaluated. Our study shows that when using classical Western music to reduce self-perceived anxiety, tonal music should be considered. In addition, it also indicates that harmonicity is a suitable indicator of relaxing music, while the role of scoring and dynamics in reducing non-pathological listener distress should be further investigated. Version of record

We introduce Emotion-aware Music Tower Blocks (EmoMTB), an audiovisual interface to explore large music collections. It creates a musical landscape, by adopting the metaphor of a city, where similar songs are grouped into the same building and nearby buildings form neighborhoods of particular genres. In order to personalize the user experience, an underlying classifier monitors textual user-generated content, by predicting their emotional state and adapting the audiovisual elements of the interface accordingly. EmoMTB enables users to explore different musical styles either within their comfort zone or outside of it. Besides, tailoring the results of the recommender engine to match the affective state of the user, EmoMTB offers a unique way to discover and enjoy music. EmoMTB supports exploring a collection of circa half a million streamed songs using a regular smartphone as a control interface to navigate in the landscape. Linz Institute of Technology LIT-ARS-2020-015 Fonds zur Förderung der Wissenschaftlichen Forschung (DE-588)2054142-9 P33526 Version of record

When we appreciate a piece of music, it is most naturally because of its content, including rhythmic, tonal, and timbral elements as well as its lyrics and semantics. This suggests that the human affinity for music is inherently content-driven. This kind of information is, however, still frequently neglected by mainstream recommendation models based on collaborative filtering that rely solely on user-item interactions to recommend items to users. A major reason for this neglect is the lack of standardized datasets that provide both collaborative and content information. The work at hand addresses this shortcoming by introducing Music4All-Onion, a large-scale, multi-modal music dataset. The dataset expands the Music4All dataset by including 26 additional audio, video, and metadata characteristics for 109,269 music pieces. In addition, it provides a set of 252,984,396 listening records of 119,140 users, extracted from the online music platform Last.fm, which allows leveraging user-item interactions as well. We organize distinct item content features in an onion model according to their semantics, and perform a comprehensive examination of the impact of different layers of this model (e.g., audio features, user-generated content, and derivative content) on content-driven music recommendation, demonstrating how various content features influence accuracy, novelty, and fairness of music recommendation systems. In summary, with Music4All-Onion, we seek to bridge the gap between collaborative filtering music recommender systems and content-centric music recommendation requirements. Fonds zur Förderung der Wissenschaftlichen Forschung P33526 DFH-23 Version of record

The extent to which the sequence of tracks in music playlists matters to listeners is a disputed question, nevertheless a very important one for tasks such as music recommendation (e.g., automatic playlist generation or continuation). While several user studies already approached this question, results are largely inconsistent. In contrast, in this paper we take a data-driven approach and investigate 704,166 user-generated playlists of a major music streaming provider. In particular, we study the consistency (in terms of variance) of a variety of audio features and metadata between subsequent tracks in playlists, and we relate this variance to the corresponding variance computed on a position-independent set of tracks. Our results show that some features vary on average up to 16% less among subsequent tracks in comparison to position-independent pairs of tracks. Furthermore, we show that even pairs of tracks that lie up to 12 positions apart in the playlist are significantly more consistent in several audio features and genres. Our findings yield a better understanding of how users create playlists and will stimulate further progress in sequential music recommenders.

Although recommender systems (RSs) play a crucial role in our society, previous studies have revealed that the performance of RSs may considerably differ between groups of individuals with different characteristics or from different demographics. In this case, a RS is considered to be unfair when it does not perform equally well for different groups of users. Considering the importance of RSs in the distribution and consumption of musical content worldwide, a careful evaluation of fairness in the context of music RSs is crucial. To this end, we first introduce LFM-2b, a novel large-scale real-world dataset of music listening records, comprising a subset to investigate bias of RSs regarding users demographics. We then define a notion of fairness based on the performance gap of a RS between the users with different demographics, and evaluate a variety of collaborative filtering algorithms in terms of accuracy and beyond-accuracy metrics to explore the fairness in the RS results toward a specific gender group. We observe the existence of significant discrepancies (unfairness) between the performance of algorithms across male and female user groups. Based on these discrepancies, we explore to what extent recommender algorithms lead to intensifying the underlying population bias in the final results. We also study the effect of a resampling strategy, commonly used as debiasing method , which yields slight improvements in the fairness measures of various algorithms while maintaining their accuracy and beyond-accuracy performance. (VLID)6299080 Version of record

Conversion issues across musical symbolic representations, such as musicXML, MEI, and humdrum, are well known. Often, these depend on methodological choices undertaken during the generation and processing of the data. For a better under-standing of this topic, we present a transcription protocol, result of trial and error transcription attempts performed with Finale engraving software, which aims to prevent conversion errors (Verovio 2.1.0 and VHV were taken into account for conversion) from musicXML (export format from Finale) to MEI and **kern (symbolic representations also evaluated).