Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics

TL;DR

This study explores how listener, artist, and track characteristics influence perceptions of diversity in electronic music, comparing human judgments with computational diversity measures and examining the effects of domain knowledge and familiarity.

Contribution

It provides insights into the relationship between perceived and computational diversity in music, highlighting the influence of listener background and introducing a user-study methodology.

Findings

Computational models align with listener choices when agreement is high.

Differences in domain knowledge and familiarity affect diversity perception.

Models show mixed results when listener agreement is low.

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.