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dc.contributor.authorImbrasaitė, Vaiva
dc.contributor.authorRobinson, Peter
dc.date.accessioned2013-05-30T05:07:49Z
dc.date.available2013-05-30T05:07:49Z
dc.date.issued2013
dc.identifier.citationImbrasaitė, V. & Robinson, P. (2013). Absolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music. In: Proceedings of the 3rd International Conference on Music & Emotion (ICME3), Jyväskylä, Finland, 11th - 15th June 2013. Geoff Luck & Olivier Brabant (Eds.). University of Jyväskylä, Department of Music.
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/41638
dc.description.abstractIt is believed that violation of or conformity to expectancy when listening to music is one of the main sources of musical emotion. To address this, we test a new way of building feature vectors and representing features within the vector for the machine learning approach to continuous emotion tracking systems. Instead of looking at the absolute values for specific features, we concentrate on the average value of that feature across the whole song and the difference between that and the absolute value for a particular sample. To test this “relative” representation, we used a corpus of popular music with continuous labels on the arousalvalence space. The model consists of a Support Vector Regression classifier for each axis, with one feature vector for each second of a song. The relative representation, when compared to the standard way of building feature vectors, gives a 10% improvement on average (and up to 25% improvement for some models) on the explained variance for both the valence and arousal axes. We also show that this result isen
dc.language.isoeng
dc.publisherUniversity of Jyväskylä, Department of Music
dc.relation.ispartofProceedings of the 3rd International Conference on Music & Emotion (ICME3), Jyväskylä, Finland, 11th - 15th June 2013. Geoff Luck & Olivier Brabant (Eds.). ISBN 978-951-39-5250-1
dc.rightsIn Copyright
dc.subject.otheremotions
dc.subject.othermusic
dc.subject.otheremotion tracking
dc.subject.otherdimensional space
dc.subject.othermachine learning
dc.titleAbsolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music
dc.typeconference paper
dc.identifier.urnURN:NBN:fi:jyu-201305301846
dc.type.dcmitypeText
dc.contributor.laitosMusiikin laitosfi
dc.contributor.laitosDepartment of Musicen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.type.versionpublishedVersion
dc.rights.accesslevelopenAccess
dc.relation.conferenceThe 3rd International Conference on Music & Emotion, Jyväskylä, Finland, June 11-15, 2013
dc.rights.urlhttps://rightsstatements.org/page/InC/1.0/


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