A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Clements, Emily
Saari, Pasi
Burger, Birgitta
Toiviainen, Petri

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Carlson, Emily; Saari, Pasi; Burger, Birgitta; Toiviainen, Petri

Lehti tai sarja: Journal of New Music Research

ISSN: 1744-5027

eISSN: 0929-8215

Julkaisuvuosi: 2020

Volyymi: 49

Lehden numero: 2

Artikkelin sivunumerot: 162-177

Kustantaja: Routledge

Julkaisumaa: Britannia

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/93755

Tiivistelmä

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.

YSO-asiasanat: liikkeentunnistus; koneoppiminen; musiikki; tanssi

Vapaat asiasanat: motion capture; machine learning; embodied cognition

Tieteenalat: