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.
Highlights
As humans our everyday life is guided by diurnal patterns
By leveraging the rich information in the music streaming sessions dataset (MSSD) along with two behavioural follow-up studies we show that audio features allow us to quantify distinct diurnal patterns of musical content
We have shown that the rhythms of daily life are accompanied by fluctuations in musical preference
Summary
As humans our everyday life is guided by diurnal patterns. Day, but diurnal patterns manifest in a range of biochemical and physiological systems [1,2]. These 2 rhythms stem from natural selection favouring endogenous circadian rhythms, intrinsically locked to the planet’s rotational period [3,4]. Not all these patterns follow a simple sinusoidal light–dark cycle, but instead exhibit complex behaviours such as transitional periods and time-lagged interactions [5]
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