Music we move to: Spotify audio features and reasons for listening

Abstract

Previous literature has shown that music preferences (and thus preferred musical features) differ depending on the listening context and reasons for listening (RL). Yet, to our knowledge no research has investigated how features of music that people dance or move to relate to particular RL. Consequently, in two online surveys, participants (N = 173) were asked to name songs they move to (“dance music”). Additionally, participants (N = 105) from Survey 1 provided RL for their selected songs. To investigate relationships between the two, we first extracted audio features from dance music using the Spotify API and compared those features with a baseline dataset that is considered to represent music in general. Analyses revealed that, compared to the baseline, the dance music dataset had significantly higher levels of energy, danceability, valence, and loudness, and lower speechiness, instrumentalness and acousticness. Second, to identify potential subgroups of dance music, a cluster analysis was performed on its Spotify audio features. Results of this cluster analysis suggested five subgroups of dance music with varying combinations of Spotify audio features: “fast-lyrical”, “sad-instrumental”, “soft-acoustic”, “sad-energy”, and “happy-energy”. Third, a factor analysis revealed three main RL categories: “achieving self-awareness”, “regulation of arousal and mood”, and “expression of social relatedness”. Finally, we identified variations in people’s RL ratings for each subgroup of dance music. This suggests that certain characteristics of dance music are more suitable for listeners’ particular RL, which shape their music preferences. Importantly, the highest-rated RL items for dance music belonged to the “regulation of mood and arousal” category. This might be interpreted as the main function of dance music. We hope that future research will elaborate on connections between musical qualities of dance music and particular music listening functions.

A text mining approach to the use of "groove" in everyday language

“…Based on 970,220 comments on 155 music videos, our findings suggest that the term groove is used to describe movement-and pleasure-related subjective experiences, as well as objective musical qualities that are tightly linked to the genres funk, soul, and R&B. Resonating with previous studies, groove terms were very likely to co-occur with movement (e.g., Janata et al, 2012;Madison, 2006;Stupacher et al, 2013), timing (e.g., Keil, 1995Senn et al, 2016;Witek et al, 2014), and flow/smoothness (Duman et al, 2021;Stupacher 2019) themes. Our dataset did not allow for conclusions about the link between groove and social bonding, which has been proposed by previous studies (Dotov et al, 2021;Duman et al, 2021Duman et al, , 2022Stupacher et al, 2022). In general, the study shows that text-mining approaches to analyzing YouTube comments provide an interesting perspective on how a general population of listeners discusses music.…”

“…Recent studies suggest that the mere juxtaposition of objective musical causes and subjective psychological effects may be too simplistic to fully capture the groove phenomenon. Studies have shown that the groove experience is moderated by the personal background of the listener, such as their musical taste or familiarity with the repertoire (Senn et al, 2021), by the concrete listening situation, such as live vs. recorded music (Swarbrick et al, 2019), and by feelings of social connectedness, immersion, flow, and energetic arousal (Dotov et al, 2021;Duman et al, 2021Duman et al, , 2022Kowalewski et al, 2020;Stupacher, 2019;Senn et al, in press), indicating that the groove experience is complex and multifaceted (Senn et al, 2019).…”

Groove as a multidimensional participatory experience

“…Alternatively, this finding might hint at the existence of different types of groove experiences (as also hinted in Hosken [2020] and elaborated further in Bechtold & Witek, 2021). Different kinds of groove and their relation with experience of time have earlier been proposed by Keil (1995, p. 8): “each person has a unique feel for time and that bringing different or discrepant personalities together generates different kinds of groove.” Moreover, while it is well known that dance songs typically have a tempo of around 120 bpm (Duman et al, 2022; Moelants, 2002), a recent study reported five subgroups of dance music (with various combinations of Spotify audio features, including tempo) associated with different listening reasons (Duman et al, 2022). This might also be related to why people embody music in various ways during spontaneous dance (Toiviainen & Carlson, 2022; Toiviainen et al, 2010).…”

“…Ten Item Personality Index (TIPI: Gosling et al, 2003), Short Test of Music Preferences (STOMP: Rentfrow & Gosling, 2003), and a 21-item questionnaire of reasons for music listening (Duman et al, 2022).…”

Information Retrieval Technologies and Big Data Analytics to Analyze Product Innovation in the Music Industry

“…The MIR tool Essentia provides a list of computational features that can be used to characterize music compositions (see the Methodology section for more details) [33,34]. Furthermore, recently, Spotify, the Swedish audio streaming service used by about 456 million users [35], has developed a list of music features, covering mainly perceptual features, that can be used to complement those offered by Essentia (see the Methodology section for more details) and that has been increasingly adopted in the academic literature (e.g., [36][37][38]). By integrating the features provided by Essentia and Spotify, we defined a framework of features that covers both computational and perceptual ones and allow to describe a music composition more comprehensively.…”