Automatic music mood recognition using Russell's twodimensional valence-arousal space from audio and lyrical data as classified using SVM and Naïve Bayes

K.R. Tan; M.L. Villarino; C. Maderazo

doi:10.1088/1757-899X/482/1/012019

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Automatic music mood recognition using Russell's twodimensional valence-arousal space from audio and lyrical data as classified using SVM and Naïve Bayes

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K.R. Tan, M.L. Villarino, C. Maderazo

2019 IOP Conference Series: Materials Science and Engineering Vol. 482 Issue 1 Conference paper Cited by 6

Abstract

Automatic music mood recognition is still a new field of research that is gaining attention in the last decade. This study created a system that predicts which of the four quadrants of the valence-arousal space the song belongs to. The system used support-vector machine (SVM) for audio features while Naïve Bayes was used for lyrical features. audio classification achieved a high accuracy for arousal while lyrics classification achieved a high accuracy for valence. © 2019 Institute of Physics Publishing. All rights reserved.

Affiliations

Department of Computer and Information Sciences-University of San Carlos-Talamban Campus, Cebu City, Philippines

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