summary: While most people prefer to drift off to sleep listening to calmer, slower songs, some feel more relaxed listening to familiar, high-energy folk music.
A new study has identified several defining characteristics of sleep-related music, such as being quieter and slower than other music.
However, popular sleep music playlists on Spotify also include faster, louder, and more energetic tracks. Rebecca Jane Skarat of Aarhus University, Denmark, and colleagues report these findings in the journal Open Access Plus one On January 18, 2023.
Many people say they listen to music to help them sleep, which raises the question of whether the music chosen for this purpose shares some universal characteristics. However, research on the properties of sleep music is limited, and previous studies tended to be relatively small.
To better understand the characteristics of sleep music, Skarat and colleagues analyzed 225,626 tracks from 985 sleep-related Spotify playlists. They used the Spotify API to compare the sound features of sleep tracks to the sound features of music from a dataset that is representative of music in general.
This analysis showed that sleep music tends to be quieter and slower than other music. It also often lacks lyrics and often features vocal instrumentation. However, despite these trends, researchers have found great diversity in the musical features of sleep music, and have identified six distinct subcategories.
Three subcategories, including ambient music, correspond to specific typical characteristics of sleep music.
However, the music in the other three subcategories was louder and had a higher energy score than regular sleep music. These songs included several popular songs, including BTS’s “Dynamite,” and Billie Eilish and Khaled’s “Beautiful (With Khaled).”
The authors speculate that despite their high energy, folk songs can help some people relax and fall asleep through their knowledge. However, more research will be needed to explore this possibility and identify the different reasons why different people choose different sleep music.
Overall, this study suggests that there is no “one size fits all” when it comes to the music people choose to sleep with. The findings could help develop music-based strategies in the future to help people fall asleep.
The authors add: “In this study, we investigated the characteristics of music used for sleep and found that although sleep music is generally softer, slower, instrumental, and performed on vocal instruments than other music, the music people use for sleep displays a wide variety including: Music that is high energy and rhythmic.
“The study could inform the clinical use of music and advance our understanding of how music is used to regulate human behavior in everyday life.”
About this sleep news and music research
author: Hanna Abdullah
Contact: Hanna Abdullah – Plus
picture: The image is in the public domain
Original search: open access.
“Acoustic features of sleep music: general characteristics and subgroupBy Kira Vaib Jespersen. Plus one
Acoustic features of sleep music: general characteristics and subgroup
Throughout history, lullabies have been used to help babies sleep, and today, with the increased accessibility of recorded music, many people report listening to music as a tool for improving sleep. However, we know very little about this common human habit.
In this study, we elucidated the properties of sleep-related music by extracting audio features from a large number of tracks (N = 225,626) retrieved from sleep playlists of the global streaming platform Spotify. Compared to music in general, we found sleep music to be quieter and slower; It was often instrumental (that is, without words) and played on vocal instruments.
However, there was a significant amount of variation in sleep music, which clustered into six distinct subgroups. Remarkably, three of the subsets included common pieces that were faster, louder, and more energetic than normal sleep music.
The results reveal previously unknown aspects of the acoustic features of sleep music and highlight individual variation in the choice of music used for sleep.
Using digital traces, we were able to identify general and subset characteristics of sleep music in a unique global dataset, advancing our understanding of how humans use music to regulate their behavior in daily life.