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WP1b: Sonic-Social Genre: Towards Multimodal Computational Music Genre Modelling

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  • WP1a: Music Recommender Systems and the Development of Aesthetic Experience
  • Current page: WP1b: Sonic-Social Genre: Towards Multimodal Computational Music Genre Modelling
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  • WP1b: Sonic-Social Genre: Towards Multimodal Computational Music Genre Modelling

Old-Fashioned Computer

Georgina Born, Department of Anthropology and Institute of Advanced Studies, UCL

Owen Green, Postdoctoral Fellow, Max Planck Institute for Empirical Aesthetics, Frankfurt, and Department of Anthropology, UCL

Bob L. T. Sturm, KTH Royal Institute of Technology, Stockholm

Melanie Wald-Fuhrmann, Max Planck Institute for Empirical Aesthetics, Frankfurt

A core topic of music informatics is music ‘similarity’, which is often approached by the proxy task of music genre classification. This proxy task however lacks an adequate theory of genre, as well as meaningful experimental methods for evaluating music listening algorithms. This study addresses these problems through innovative collaborations between music informatics engineers, musicologists, and music anthropologists and sociologists.

The aim is to examine how the nature of genre is and is not compatible with computational methods. One branch of this work has been to conduct and publish an up to date survey of the current state of the art in music genre classification (Green et al. 2024). The second branch has been to develop a rich case study of a musically, socially and culturally complex musical genre – 1990s Jungle Drum n’ Bass – as a prism through which to think through the challenges of computational modelling, and to suggest methodological possibilities for incorporating new challenges and perspectives from musicology, anthropology and sociology of music, and science and technology studies into music informatics.

Outputs

Sonic Social Genre Workshop, Max Plank Institute for Empirical Aesthetics, Frankfurt, Germany, 9-12 May 2023

This three day workshop brought together scholars interested in genre from the social sciences, humanities and computer sciences to map out the complexities of genre as a domain and the practical challenges in its computational modelling.

Publications:

Green, O., Sturm, B. L. T., Born, G., & Wald-Fuhrmann, M. (2024). A critical survey of research in music genre recognition. Proceedings of the International Society for Music Information Retrieval. https://ismir2024program.ismir.net/poster_149.html

Green, O., & Born, G. (forthcoming). What’s (In) a Genre? Sounds, Socialities and Time in Early Jungle Drum & Bass. In G. Born & D. Brackett (Eds.), Genre and Music: New Directions. Duke University Press.

Green, O. (forthcoming) ‘Towards Computing Genre’, Cambridge Forum on AI: Culture and Society

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