Towards Predicting Expressed Emotion in Music from Pairwise Comparisons

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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We introduce five regression models for the modeling of expressed emotion in music using data obtained in a two alternative forced choice listening experiment. The predictive performance of the proposed models is compared using learning curves, showing that all models converge to produce a similar classification error. The predictive ranking of the models is compared using Kendall's tau rank correlation coefficient which shows a difference despite similar classification error. The variation in predictions across subjects and the difference in ranking is investigated visually in the arousal-valence space and quantified using Kendall's tau.
Original languageEnglish
Title of host publicationProceedings of the 9th Sound and Music Computing Conference
Publication date2012
Pages350-357
StatePublished

Conference

Conference9th Sound and Music Computing Conference (SMC 2012)
CountryDenmark
CityCopenhagen
Period11/07/1214/07/12
Internet addresshttp://smc2012.smcnetwork.org/

Bibliographical note

This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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