People listen to music streaming for specific blocks of time


Figure 1. The diurnal patterns of the audio characteristics correspond to five subdivisions of the day. (a) Normalized audio characteristics plotted over the entire week. Using k-means clustering, we found an optimal division into five distinct clusters, shown here as colored overlays on the graph. We labeled the temporal occurrence of the clusters by first calculating the start of the mode, and then using a descriptive term for the time of day. The temporal occurrences of the clusters were always sequential and covered very similar subdivisions of the day. We illustrate the start and shift of the mode of each subdivision in the illustrations of the clock. (b) To highlight the diurnal cycles of the audio characteristics, here we show three selected audio characteristics, tempo, volume and dance, over 24 hours starting at 6:00 a.m. Normalized audio characteristics are presented as an average over all days of the week and the shaded area indicates the 95% confidence interval. Credit: DOI: 10.1098 / rsos.210885

A trio of researchers from Aarhus University and the Royal Academy of Music Aarhus / Aalborg found that people who listen to music streaming on online services such as Spotify tend to do so for the same specific times each day, although there is more variation during the weekends. In their article published in the journal Royal Society Open Science, Ole Adrian Heggli, Jan Stupacher and Peter Vuust describe the results of their analysis of Spotify data.

Over the past few decades, the act of listening to music has changed as playback technology has changed, from phonographs to tape recorders, CDs and now digital players. In this new effort, the researchers questioned the listening habits of Spotify subscribers, most of whom listen with their smartphones. To find out more, they obtained and analyzed over 2 billion data points from the streaming service.

The researchers identified a pattern: Spotify users listened to music during different but specific time periods. They discovered that there were five such time slots: morning, afternoon, later evening, night, and late evening until the early hours of the morning. Interestingly, the time blocks were similar across Spotify’s user base. Not everyone listened to the same songs, of course, or even the same genre, but there were other notable models. People tended to turn the volume down in the morning, for example, and then turn it up as they went through the morning. Most tended to keep the volume higher until they hit their nighttime block. They also found that the tempo of the music people listened to was slow during the day, but then accelerated at night, eventually turning into music suitable for dancing.

The researchers also conducted an experiment that allowed them to track the gambling habits of 89 known volunteers. Data from this experiment revealed that users were more likely to listen to playlists they created on their own than to enjoy those created by Spotify’s algorithms. However, they were unable to detect patterns in such playlists. They conclude by suggesting that the musical preferences and playing habits of regular music listeners tend to lead to cyclical listening patterns.


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More information:
Ole Adrian Heggli et al, Daytime fluctuations in musical preferences, Royal Society Open Science (2021). DOI: 10.1098 / rsos.210885

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Quote: People Listen to Music Streaming During Specific Blocks of Time (2021, November 15) Retrieved November 15, 2021 from https://phys.org/news/2021-11-people-streamed-music-specific -blocks.html

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