Diurnal fluctuations in musical preference

Abstract

The rhythm of human life is governed by diurnal cycles, as a result of endogenous circadian processes evolved to maximize biological fitness. Even complex aspects of daily life, such as affective states, exhibit systematic diurnal patterns which in turn influence behaviour. As a result, previous research has identified population-level diurnal patterns in affective preference for music. By analysing audio features from over two billion music streaming events on Spotify, we find that the music people listen to divides into five distinct time blocks corresponding to morning, afternoon, evening, night and late night/early morning. By integrating an artificial neural network with Spotify's API, we show a general awareness of diurnal preference in playlists, which is not present to the same extent for individual tracks. Our results demonstrate how music intertwines with our daily lives and highlight how even something as individual as musical preference is influenced by underlying diurnal patterns.

Keywords

• music
• digital traces
• diurnal patterns
• music information retrieval
• audio features