期刊
JOURNAL OF NEW MUSIC RESEARCH
卷 49, 期 2, 页码 162-177出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/09298215.2020.1711778
关键词
Motion capture; machine learning; embodied cognition
资金
- Academy of Finland [272250, 299067, 274037]
- Academy of Finland (AKA) [299067, 299067] Funding Source: Academy of Finland (AKA)
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.
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