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Background
Previous research has observed vast changes in musical preference during childhood and adolescence due to developmental processes such as the experience of aesthetic responses to music (Nieminen et al., 2012) and the formation and expression of social identity through distinct musical preferences (Hargreaves et al., 2016). Most of this research, however, is either based on the evaluation of musical pieces that have been previously selected by adult experimenters, or self-reports on preferences for different musical styles.
Aim(s)
This study presents another methodological approach by collecting and analysing music that was freely requested on a children’s radio show. The aim was to test to what extent findings from self-reports and ratings of pre-selected music apply to this kind of data.
Methods
A sample of 1412 freely and publicly expressed music requests of children between 4 and 11 years was taken from a German radio program and analysed with regard to age- and sex-specific differences. The music was categorized into genres as listed on Spotify and examined by methods of Music Information Retrieval provided by the Spotify Developer API. Additionally, the music was genre-independently classified as children’s music.
Results
Results showed that, at younger ages, the requests were generally more evenly distributed across different genres. Regarding single genres, logistic regression models revealed small positive relationships between age and the likelihood of requesting the genres Pop and Electro and small negative relationships between age and the likelihood of requesting Acapella, German songwriter, Indie, theme songs, and children's music. Furthermore, contingency table analyses showed that boys requested significantly more Rock and Hip Hop, whereas girls had a higher tendency to ask for Pop. Finally, age was negatively correlated with the Spotify features valence and liveness of the requested music, which was related to the higher preference for children’s music at younger ages.
Discussion and Conclusion
The results suggest that previous findings regarding musically expressed gender roles and increasing formation of distinct genre preferences during infancy also apply to single song requests made on a radio show.
References
Hargreaves, D. J., North, A. C., & Tarrant, M. (2016). How and why do musical preferences change in childhood adolescence. In G. McPherson (Ed.), The child as musician: A handbook of musical development (Second edition). Oxford University Press.
Nieminen, S., Istók, E., Brattico, E., & Tervaniemi, M. (2012). The development of the aesthetic experience of music: Preference, emotions, and beauty. Musicae Scientiae, 16(3), 372–391. https://doi.org/10.1177/1029864912450454
Exploring Album Dramaturgy: A Computational Music Analysis Across Pop Music History [Abstract]
(2024)
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-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.
The Evolution of Song Lyrics: An NLP-Based Analysis of Popular Music in Germany from 1954 to 2022
(2025)
Music is an indispensable cultural product, reflecting changes in social, psychological, and cultural contexts. This study analyzes the historical evolution of topics and conveyed affect in popular music lyrics in Germany from 1954 to 2022, using LDA-based topic modeling and transformer-based sentiment analysis. These results show that Love & Relationships is the most referenced topic, with Dreams & Longings prominent until the mid-1960s and Society & Status rising from 2017. The sentiment analysis reveals a significant decline in positive sentiment since the mid-1960s, accompanied by increases in negative, ambiguous, and neutral sentiments. These trends may reflect broader societal changes, including shifts in cultural values, rising individualism, and increasing mental health issues. The study highlights the evolving nature of popular music and its reflection of social dynamics.