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Music Emotion Maps in Arousal-Valence Space

Abstract : In this article we present the approach in which the detection of emotion is modeled by the pertinent regression problem. Conducting experiments required building a database, annotation of samples by music experts, construction of regressors, attribute selection, and analysis of selected musical compositions. We obtained a satisfactory correlation coefficient value for SVM for regression algorithm at 0.88 for arousal and 0.74 for valence. The result applying regressors are emotion maps of the musical compositions. They provide new knowledge about the distribution of emotions in musical compositions. They reveal new knowledge that had only been available to music experts until this point.
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Submitted on : Friday, November 17, 2017 - 3:46:00 PM
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Jacek Grekow. Music Emotion Maps in Arousal-Valence Space. 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2016, Vilnius, Lithuania. pp.697-706, ⟨10.1007/978-3-319-45378-1_60⟩. ⟨hal-01637515⟩



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