2020

Baltazar, Margarida; Västfjäll, Daniel

How we listen to music has been changing rapidly in the last years, with online streaming becoming more predominant. Besides the gain in accessibility for the listeners, the growth of online services also affords easier access to data for musical analyses. A growing body of research has been showing that daily life music listening serves varied functions, from affect regulation to social bonding. More specifically, the reduction of stress responses is quite pertinent in the contemporary world, and recent studies have high-lighted the importance of adequate musical choices. This study aimed to identify the characteristics of music that individuals perceive as favorable to relax and to compare it to the music perceived as unfa-vorable to relax. Furthermore, the study intended to explore the possibilities offered by the application programming interfaces (API) of services such as the music streaming Spotify and the lyrics database genius as sources for future work. Answers were collected through an online survey, where the participants provided examples of music tracks (favorable and unfavorable to relaxation). They also rated the contribution of several musical mechanisms to the (in)efficacy of the examples. Musical features were pulled from the Spotify API and the lyrics were retrieved from the genius API through the R package spotifyr and then analyzed. The discriminant functions for musical features and perceived mechanisms (Wilks’ lambda: .611, χ2(20) = 257.57, p < .001) and for all the variables when lyrics were present (Wilks’ lambda: .555, χ2(26) = 202.80, p < .001) were statis-tically significant. Relaxing and non-relaxing music was successfully distinguished by perceived mechanisms, Spotify features, and two variables related to lyrics. The largest contributors for the discriminant function were the mechanisms aesthetic value, genre/preference, and familiarity, following by the Spotify features energy and loudness.