Show simple item record

dc.contributor.authorCarlson, Emily
dc.contributor.authorSaari, Pasi
dc.contributor.authorBurger, Birgitta
dc.contributor.authorToiviainen, Petri
dc.date.accessioned2024-02-29T08:21:23Z
dc.date.available2024-02-29T08:21:23Z
dc.date.issued2020
dc.identifier.citationCarlson, 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. <i>Journal of New Music Research</i>, <i>49</i>(2), 162-177. <a href="https://doi.org/10.1080/09298215.2020.1711778" target="_blank">https://doi.org/10.1080/09298215.2020.1711778</a>
dc.identifier.otherCONVID_34177070
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/93755
dc.description.abstractMachine 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.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherRoutledge
dc.relation.ispartofseriesJournal of New Music Research
dc.rightsCC BY-NC 4.0
dc.subject.othermotion capture
dc.subject.othermachine learning
dc.subject.otherembodied cognition
dc.titleDance to your own drum : identification of musical genre and individual dancer from motion capture using machine learning
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202402292227
dc.contributor.laitosMusiikin, taiteen ja kulttuurin tutkimuksen laitosfi
dc.contributor.laitosDepartment of Music, Art and Culture Studiesen
dc.contributor.oppiaineMusiikkitiedefi
dc.contributor.oppiaineMusicologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange162-177
dc.relation.issn1744-5027
dc.relation.numberinseries2
dc.relation.volume49
dc.type.versionacceptedVersion
dc.rights.copyright© 2020 Taylor & Francis
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber299067
dc.relation.grantnumber272250
dc.subject.ysokoneoppiminen
dc.subject.ysoliikkeentunnistus
dc.subject.ysotanssi
dc.subject.ysomusiikki
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p24599
jyx.subject.urihttp://www.yso.fi/onto/yso/p1278
jyx.subject.urihttp://www.yso.fi/onto/yso/p1808
dc.rights.urlhttps://creativecommons.org/licenses/by-nc/4.0/
dc.relation.doi10.1080/09298215.2020.1711778
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramPostdoctoral Researcher, AoFen
jyx.fundingprogramResearch post as Academy Professor, AoFen
jyx.fundingprogramTutkijatohtori, SAfi
jyx.fundingprogramAkatemiaprofessorin tehtävä, SAfi
jyx.fundinginformationThis work was supported by funding from the Academy of Finland, project numbers 272250, 299067 and 274037.
dc.type.okmA1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC BY-NC 4.0
Except where otherwise noted, this item's license is described as CC BY-NC 4.0