  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.
