Dance to your own drum: Identification of musical genre and individual dancer from motion capture using machine learning

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 capture
• machine learning
• embodied cognition

ORCID

Emily Carlson http://orcid.org/0000-0001-7174-2202

Birgitta Burger http://orcid.org/0000-0002-0694-3582

Additional information

Funding

This work was supported by funding from the Academy of Finland, project numbers 272250, 299067 and 274037.