Dance to your own drum : identification of musical genre and individual dancer from motion capture using machine learning
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
Julkaistu sarjassa
Journal of New Music ResearchPäivämäärä
2020Tekijänoikeudet
© 2020 Taylor & Francis
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.
Julkaisija
RoutledgeISSN Hae Julkaisufoorumista
1744-5027Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/34177070
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Tutkijatohtori, SA; Akatemiaprofessorin tehtävä, SALisätietoja rahoituksesta
This work was supported by funding from the Academy of Finland, project numbers 272250, 299067 and 274037.Lisenssi
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