The audio features of sleep music: Universal and subgroup characteristics

Abstract

Throughout history, lullabies have been used to help children sleep, and today, with the increasing accessibility of recorded music, many people report listening to music as a tool to improve sleep. Nevertheless, we know very little about this common human habit. In this study, we elucidated the characteristics of music associated with sleep by extracting audio features from a large number of tracks (N = 225,626) retrieved from sleep playlists at the global streaming platform Spotify. Compared to music in general, we found that sleep music was softer and slower; it was more often instrumental (i.e. without lyrics) and played on acoustic instruments. Yet, a large amount of variation was present in sleep music, which clustered into six distinct subgroups. Strikingly, three of the subgroups included popular tracks that were faster, louder, and more energetic than average sleep music. The findings reveal previously unknown aspects of the audio features of sleep music and highlight the individual variation in the choice of music used for sleep. By using digital traces, we were able to determine the universal and subgroup characteristics of sleep music in a unique, global dataset, advancing our understanding of how humans use music to regulate their behaviour in everyday life.

A Comparison of Music Characteristics of Funeral Music from Croatia, the Netherlands, and the United Kingdom

“…These audio features, or music characteristics, have been used before with funeral music (Hanser et al, 2022). Additionally, these measures have been used to characterize dance music (Duman et al, 2022), music that promotes sleep (Scarratt et al, 2023), music that is used in pain management (Howlin & Rooney, 2021); and to investigate moodregulation through music (Liew et al, 2022;Vidas et al, 2021).…”

“…First, although Spotify provides basic descriptions of what its music audio features mean (Spotify for Developers, 2022), how these scores are compiled is unclear. Several studies have, however, linked these values to characterize music used for a specific behavior, functions, and certain situations, such as pain management, funeral rituals, leisure, and mood regulation (Duman et al, 2022;Hanser et al, 2022;Howlin & Rooney, 2021;Liew et al, 2022;Mollenhorst et al, 2016;Scarratt et al, 2023;Vidas et al, 2021). Still, carefully considering Spotify values concerning existing databases of, for example, values for music valence, or different methods of extracting audio features from a piece of music is necessary to investigate their validity.…”

Episode model: The functional approach to emotional experiences of music

“…We also envisage that the episode model offers a fruitful set of structures and hypotheses for Music Information Retrieval research; some episodes such as EDR and FM are likely to be present in the actual patterns of music and its use (e.g., Gómez-Cañón et al, 2021;Knees, Schedl, & Goto, 2020). Episodes themselves may also be a useful target for predicting the broad musical and acoustic correlates of specific music corpora (Mehr et al, 2019;Scarratt, Heggli, Vuust, & Jespersen, 2023).…”

Music and sleep hygiene interventions for pregnancy-related insomnia: an online randomized controlled trial

“…Thereby, individual preferences were taken into account and participants were given a sense of agency. All playlists included instrumental music, except the 'Pop-ballads' playlist that was inspired by the most popular sleep music tracks from Spotify sleep playlists (Scarratt, Heggli, Vuust, & Jespersen, 2023).…”