A1 Journal article (refereed)
Dance to your own drum : identification of musical genre and individual dancer from motion capture using machine learning (2020)

Carlson, E., Saari, P., Burger, B., & Toiviainen, P. (2020). Dance to your own drum : identification of musical genre and individual dancer from motion capture using machine learning. Journal of New Music Research, 49(2), 162-177. https://doi.org/10.1080/09298215.2020.1711778

JYU authors or editors

Publication details

All authors or editors: Carlson, Emily; Saari, Pasi; Burger, Birgitta; Toiviainen, Petri

Journal or series: Journal of New Music Research

ISSN: 1744-5027

eISSN: 0929-8215

Publication year: 2020

Volume: 49

Issue number: 2

Pages range: 162-177

Publisher: Routledge

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1080/09298215.2020.1711778

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/93755

Abstract

Machine learning has been used to accurately classify musical genre using features derived from audio signals. Musical genre, as well as lower-level audio features of music, have also been shown to influence music-induced movement, however, the degree to which such movements are genre-specific has not been explored. The current paper addresses this using motion capture data from participants dancing freely to eight genres. Using a Support Vector Machine model, data were classified by genre and by individual dancer. Against expectations, individual classification was notably more accurate than genre classification. Results are discussed in terms of embodied cognition and culture.

Keywords: motion detection; machine learning; music; dance (performing arts)

Free keywords: motion capture; machine learning; embodied cognition

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 3