Intelligibility of music playlists
University College Cork
A common strategy for organising music is by arranging songs in a playlist to obtain a continuous and thematic music flow. Playlists are popular in music streaming services, where 58% of the listeners construct their own playlists. The flip side of popularity is content-overload; streaming services currently host billions of playlists. The commercial value of playlists has attracted notable research efforts during the last two decades. Much of the research on playlists is concerned with automatically constructing playlists. This dissertation is on playlists, but on a topic complementary to constructing playlists. Our concern here is on describing playlists, so that playlists can be understood by a human audience, i.e. so that they become intelligible. The way we achieve intelligibility is by developing algorithms that can generate textual annotations, both at playlist level and at song level. At playlist level, an annotation can be text (e.g. a tag or a caption) that describes the playlist as a whole; at song level, an annotation can be text that describes the transition between two consecutive songs in the playlist. The purpose of intelligibility is that of facilitating music organisation & access, as well as enhancing the listen- ing experience of users, two goals particularly relevant in a content overload scenario. We propose five algorithms for playlist-level intelligibility, and three algorithms for song-level intelligibility. We are particularly interested in the user experi- ence, so we test the algorithms, in most cases, with both offline experiments and user trials. We find evidence that the algorithms can help accomplish the two goals of intelligibility, i.e. enhancing listening experiences, and facilitating organisation and access. We pair the algorithms with a comprehensive survey of MIR research on music playlists, which provide a useful framework for understanding our contributions in the context of a broad selection of related research.
Music information retrieval , Playlists , Intelligibility , Recommender systems
Gabbolini , G. 2023. Intelligibility of music playlists. PhD Thesis, University College Cork.