The rhythm of Mexico: an exploratory data analysis of Spotify’s top 50

Pérez-Verdejo, J. Manuel; Piña-García, C. A.; Ojeda, Mario Miguel; Rivera-Lara, A.; Méndez-Morales, L.

doi:10.1007/s42001-020-00070-z

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Musical audio features are leveraged extensively in music recommender systems, such as those utilized by music streaming platforms such as Spotify. This provides researchers convenient access to audio feature data, without the need to run complex music information retrieval procedures themselves [58,59]. Audio feature information for each reported song title was obtained via the Spotify application programming interface (API; https:// devel oper.…”

Section: Audio Featuresmentioning

confidence: 99%

The sound of stress recovery: an exploratory study of self-selected music listening after stress

et al. 2023

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Background Empirical support for the notion that music listening is beneficial for stress recovery is inconclusive, potentially due to the methodological diversity with which the effects of music on stress recovery have been investigated. Little is presently known about which recovery activities are chosen by individuals for the purpose of stress recovery, and whether audio feature commonalities exist between different songs that are selected by individuals for the purpose of stress recovery. The current pre-registered study investigated whether audio feature commonalities can be extracted from self-selected songs for the purpose of stress recovery. Furthermore, the present study exploratorily examined the relationship between audio features and participants’ desired recovery-related emotions while listening and after listening to self-selected music. Methods Participants (N = 470) completed an online survey in which they described what music they would listen to unwind from a hypothetical stressful event. Data analysis was conducted using a split-sample procedure. A k-medoid cluster analysis was conducted to identify audio feature commonalities between self-selected songs. Multiple regression analyses were conducted to examine the relationship between audio features and desired recovery emotions. Results Participants valued music listening as a recovery activity to a similar extent as watching TV, sleeping, or talking to a significant other. Cluster analyses revealed that self-selected songs for the purpose of stress recovery can be grouped into two distinct categories. The two categories of songs shared similarities in key, loudness, speechiness, acousticness, instrumentalness, liveness, musical valence, tempo, duration, and time signature, and were distinguished by danceability, energy, and mode. No audio features were significantly associated with participants’ desired recovery emotions. Conclusions Although a comprehensive portrait of the relationship between audio features and stress recovery still warrants further research, the present study provides a starting point for future enquiries into the nuanced effects of musical audio features on stress recovery.

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Section: Audio Featuresmentioning

confidence: 99%

The sound of stress recovery: an exploratory study of self-selected music listening after stress

et al. 2023

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“…Study 2 used a more generalized Western (United States, United Kingdom, Canada, Australia)–East Asian (Japan, Taiwan, Hong Kong, Singapore) cultural comparison ( N = 800 songs) from official Spotify Top 50 charts. Charts are frequently used in cultural product research (e.g., Pérez-Verdejo et al, 2021) as they function as selection processes that capture the prevalent musical preferences of a culture. Following Study 1’s results, we hypothesized that East Asian charts would have higher instrumentalness, but Western charts would have higher danceability.…”

Section: Exploring Cultural Differences In Music Featuresmentioning

confidence: 99%

Groovin’ to the cultural beat: Preferences for danceable music represent cultural affordances for high-arousal negative emotions.

Liew¹,

Koh²,

Fram³

et al. 2023

Psychology of Aesthetics, Creativity, and the

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Music is a product of culture. Cross-cultural examinations of music features can reveal novel information about the cultural psychological processes involved in shaping music preferences. In Studies 1 and 2, we first identified differences in music preferences through machine learning of East-Asian and Western popular music on Spotify (combined N = 1,006,644). In interpreting these results, we developed a theory on danceability as a music feature, that represents cultural affordances for high-arousal emotions. Subsequent confirmatory studies (Studies 3–5, combined Nsongs = 3,343, Nparticipants = 495, Ncountries = 60) tested this theory by examining danceability and the role of emotion in music preferences. Specifically, we found that danceability represents cultural affordances for high-arousal negative (HAN) emotions: societies with greater HAN emotion prevalence generally prefer listening to more danceable music. Consistently, this was also observed more in independent individuals and culturally looser countries. Using evidence from Japanese and American participants (Study 5), we propose a mechanism through discharge regulation in music: cultures with looser cultural norms would also have more experiences of HAN emotions in daily life. Discharge regulation, which is listening to music to cathartically release HAN emotions, would then skew music preferences toward high-arousal (danceable) music to facilitate this cathartic HAN downregulation. These findings have implications for cross-cultural research by demonstrating that music features, being widely accessible and almost universally perceived, can quantify cultural tendencies toward affective (HAN emotion) norms beyond commonly used self-report paradigms.

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“…In the work of Perez-Verdejo et al [ 8 ], they analyze music consumption patterns in Mexico using streaming statistics and audio features from the music streaming platform Spotify. The authors investigate how music features correlate with the streaming metric and compare the regional (Mexican) patterns with global (worldwide) counterparts.…”

Section: Background and Related Workmentioning

confidence: 99%

Towards a Measure for Characterizing the Informational Content of Audio Signals and the Relation between Complexity and Auditory Encoding

Guerrero

Rivera-Dı́az-del-Castillo

Febres

et al. 2021

Entropy

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The accurate description of a complex process should take into account not only the interacting elements involved but also the scale of the description. Therefore, there can not be a single measure for describing the associated complexity of a process nor a single metric applicable in all scenarios. This article introduces a framework based on multiscale entropy to characterize the complexity associated with the most identifiable characteristic of songs: the melody. We are particularly interested in measuring the complexity of popular songs and identifying levels of complexity that statistically explain the listeners’ preferences. We analyze the relationship between complexity and popularity using a database of popular songs and their relative position in a preferences ranking. There is a tendency toward a positive association between complexity and acceptance (success) of a song that is, however, not significant after adjusting for multiple testing.

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The rhythm of Mexico: an exploratory data analysis of Spotify’s top 50

Cited by 7 publications

References 24 publications

The sound of stress recovery: an exploratory study of self-selected music listening after stress

The sound of stress recovery: an exploratory study of self-selected music listening after stress

Groovin’ to the cultural beat: Preferences for danceable music represent cultural affordances for high-arousal negative emotions.

Towards a Measure for Characterizing the Informational Content of Audio Signals and the Relation between Complexity and Auditory Encoding

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