Perceptions of diversity in electronic music: the impact of listener, artist, and track characteristics

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• dc.contributor.author Porcaro, Lorenzo
• dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
• dc.contributor.author Castillo, Carlos
• dc.date.accessioned 2023-02-07T13:25:05Z
• dc.date.available 2023-02-07T13:25:05Z
• dc.date.issued 2022
• dc.description.abstract Shared practices to assess the diversity of retrieval system results are still debated in the Information Retrieval community, partly because of the challenges of determining what diversity means in specific scenarios, and of understanding how diversity is perceived by end-users. The field of Music Information Retrieval is not exempt from this issue. Even if fields such as Musicology or Sociology of Music have a long tradition in questioning the representation and the impact of diversity in cultural environments, such knowledge has not been yet embedded into the design and development of music technologies. In this paper, focusing on electronic music, we investigate the characteristics of listeners, artists, and tracks that are influential in the perception of diversity. Specifically, we center our attention on 1) understanding the relationship between perceived diversity and computational methods to measure diversity, and 2) analyzing how listeners' domain knowledge and familiarity influence such perceived diversity. To accomplish this, we design a user-study in which listeners are asked to compare pairs of lists of tracks and artists, and to select the most diverse list from each pair. We compare participants' ratings with results obtained through computational models built using audio tracks' features and artist attributes. We find that such models are generally aligned with participants' choices when most of them agree that one list is more diverse than the other, while they present a mixed behaviour in cases where participants have little agreement. Moreover, we observe how differences in domain knowledge, familiarity, and demographics can influence the level of agreement among listeners, and between listeners and diversity metrics computed automatically.
• dc.description.sponsorship This work is partially supported by the European Commission under the TROMPA project (H2020 - grant agreement No. 770376). This work is also partially supported by the HUMAINT programme (Human Behaviour and Machine Intelligence), Joint Research Centre, European Commission. The project leading to these results received funding from "la Caixa" Foundation (ID 100010434), under the agreement LCF/PR/PR16/51110009.
• dc.format.mimetype application/pdf
• dc.identifier.citation Porcaro L, Gómez E, Castillo C. Perceptions of diversity in electronic music: the impact of listener, artist, and track characteristics. Proceedings of the ACM on Human-Computer Interaction. 2022;6(CSCW1):109. DOI: 10.1145/3512956
• dc.identifier.doi http://dx.doi.org/10.1145/3512956
• dc.identifier.issn 2573-0142
• dc.identifier.uri http://hdl.handle.net/10230/55669
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof Proceedings of the ACM on Human-Computer Interaction. 2022;6(CSCW1):109.
• dc.relation.isreferencedby https://github.com/LPorcaro/music-diversity-analysis
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/770376
• dc.rights © 2022 Association for Computing Machinery
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword music information retrieval
• dc.subject.keyword diversity
• dc.subject.keyword reliability
• dc.subject.keyword Information systems
• dc.subject.keyword Information retrieval
• dc.subject.keyword Human-centered computing
• dc.subject.keyword User studies
• dc.title Perceptions of diversity in electronic music: the impact of listener, artist, and track characteristics
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion