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dc.contributor.authorLaurier, Cyril
dc.contributor.authorLartillot, Olivier
dc.contributor.authorEerola, Tuomas
dc.contributor.authorToiviainen, Petri
dc.date.accessioned2009-08-03T06:24:21Z
dc.date.available2009-08-03T06:24:21Z
dc.date.issued2009
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/20889
dc.description.abstractIn this paper, we present an analysis of the associations between emotion categories and audio features automatically extracted from raw audio data. This work is based on 110 excerpts from film soundtracks evaluated by 116 listeners. This data is annotated with 5 basic emotions (fear, anger, happiness, sadness, tenderness) on a 7 points scale. Exploiting state-of-the-art Music Information Retrieval (MIR) techniques, we extract audio features of different kind: timbral, rhythmic and tonal. Among others we also compute estimations of dissonance, mode, onset rate and loudness. We study statistical relations between audio descriptors and emotion categories confirming results from psychological studies. We also use machine-learning techniques to model the emotion ratings. We create regression models based on the Support Vector Regression algorithm that can estimate the ratings with a correlation of 0.65 in average.en
dc.format.extent260-264
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.rightsIn Copyright
dc.subject.otheremotionen
dc.subject.othermusicen
dc.subject.othermiren
dc.subject.otheraudioen
dc.subject.otherclassificationen
dc.subject.othermachine learningen
dc.titleExploring relationships between audio features and emotion in musicen
dc.typeconference paper
dc.identifier.urnURN:NBN:fi:jyu-2009411271
dc.type.dcmitypeText
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.rights.accesslevelopenAccess
dc.relation.conferenceESCOM 2009 : 7th Triennial Conference of European Society for the Cognitive Sciences of Music
dc.format.contentfulltext
dc.rights.urlhttps://rightsstatements.org/page/InC/1.0/


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  • ESCOM 2009 [101]
    7th Triennial Conference of European Society for the Cognitive Sciences of Music

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