Exploring Album Dramaturgy: A Computational Music Analysis Across Pop Music History [Abstract]
- Background
A musical album can be considered a central artwork of an artist, and the order of its songs can influence the way the album is perceived by the listener. That is, purposeful sequencing can create an emotional journey and suspense (Lehne & Kölsch, 2015), tell a story or reinforce a particular concept or theme in the artist's music.
Aim(s)
This study thus aimed on examining the evolution of albums over the course of pop music history and on unveiling patterns (i.e. clusters) and temporal shifts in emotion- related album dramaturgy dependent on the cultural context (in terms of genres) by utilizing advanced computational methods.
Methods
We first created a dataset by scraping titles and artists of the weekly Top 10 albums (n = 14.394) from the Billboard.com charts from 1957 to 2022. We further extracted the emotion-related audio descriptors ‘valence’ and ‘energy’ from the Spotify Developer API for each track of the corresponding album, resulting in two-dimensionalBackground
A musical album can be considered a central artwork of an artist, and the order of its songs can influence the way the album is perceived by the listener. That is, purposeful sequencing can create an emotional journey and suspense (Lehne & Kölsch, 2015), tell a story or reinforce a particular concept or theme in the artist's music.
Aim(s)
This study thus aimed on examining the evolution of albums over the course of pop music history and on unveiling patterns (i.e. clusters) and temporal shifts in emotion- related album dramaturgy dependent on the cultural context (in terms of genres) by utilizing advanced computational methods.
Methods
We first created a dataset by scraping titles and artists of the weekly Top 10 albums (n = 14.394) from the Billboard.com charts from 1957 to 2022. We further extracted the emotion-related audio descriptors ‘valence’ and ‘energy’ from the Spotify Developer API for each track of the corresponding album, resulting in two-dimensional time series. We then employed Deep Time Series Embedding Clustering (DeTSEC; lenco et al., 2020) which can handle multivariate time series of different length. Finally, k-means clustering was applied on the embeddings produced by the DeTSEC, resulting in a 12 cluster solution.
Results
To facilitate the interpretation of our solution, we determined the most representative album of a cluster using the smallest Euclidean distance to its centroid. Preliminary time series analysis of the resulting 12 albums confirm different emotional trajectories of the respective clusters, for instance showing decreasing valence and arousal values over time for “I am up” by Young Thug (Cluster 6) or highest valence and arousal values at the beginning and the end of the album for“The New Classic” by Iggy Azalea (Cluster 12). Furthermore, Chi² tests revealed significant associations between an album’s cluster membership and both its year of release and its genre.
Discussion and Conclusion
The study constitutes a first step in shedding light on the evolution of albums in modern popular music since its emergence. It further provides an insight into how temporal dramaturgies in terms of the emotion-related arrangement of songs of albums differ across genres and undergo cultural trends and temporal shifts.
References
Ienco D, & Interdonato R (2020). Deep Multivariate Time Series Embedding Clustering via Attentive-Gated Autoencoder. Advances in Knowledge Discovery and Data Mining. 318–329
Lehne, M., & Koelsch, S. (2015). Toward a general psychological model of tension and suspense. Frontiers in Psychology, 6, 79.…


| Author: | Jochen SteffensORCiD, Patrick Blättermann, Marcel Joschko |
|---|---|
| open access: | Diamant - ohne Publikationskosten mit Lizenzhinweis |
| Institutes: | Institute of Sound and Vibration Engineering (ISAVE) |
| Fachbereich/Einrichtung: | Hochschule Düsseldorf / Fachbereich - Medien |
| Document Type: | Conference Proceeding |
| Year of Completion: | 2024 |
| Language of Publication: | German |
| Publisher: | Universität Graz |
| Place of publication: | Graz |
| Parent Title (German): | escom12 - The 12th Triennial Conference of the European Society for the Cognitive Sciences of Music: Book of Abstracts |
| Page Number: | 2 |
| First Page: | 245 |
| Last Page: | 246 |
| DOI: | https://doi.org/10.25364/602.2024.3 |
| GND Keyword: | Popmusik; Dramaturgie |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren |
| 7 Künste und Unterhaltung / 78 Musik / 780 Musik | |
| Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
| Release Date: | 2025/06/03 |
