Abstract:  Our contribution addresses popular music as essential part of media entertainment offerings. Prior works explained liking for specific music titles in ‘push scenarios’ (radio programs, music recommendation, curated playlists) by either drawing on personal genre preferences, or on findings about ‘cognitive side effects’ leading to a preference drift towards familiar and society-wide popular tracks. However, both approaches do not satisfactorily explain why previously unknown music is liked. To address this, we hypothesise that unknown music is liked the more it is perceived as emotionally and semantically expressive, a notion based on concepts from media entertainment research and popular music studies. By a secondary analysis of existing data from an EU-funded R&D project, we demonstrate that this approach is more successful in predicting 10000 listeners’ liking ratings regarding 549 tracks from different genres than all hitherto theories combined. We further show that major expression dimensions are perceived relatively homogeneous across different sociodemographic groups and countries. Finally, we exhibit that music is such a stable, non-verbal sign-carrier that a machine learning model drawing on automatic audio signal analysis is successfully able to predict significant proportions of variance in musical meaning decoding.

Keywords:  entertainment; genre preferences; musical expression; music preferences; musical taste; popular music; push scenarios; semantics

Published:   13 August 2020

Issue:   Vol 8, No 3 (2020): Computational Approaches to Media Entertainment Research