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Dimensional Music Emotion Recognition by Machine Learning

Dimensional Music Emotion Recognition by Machine Learning

Junjie Bai, Lixiao Feng, Jun Peng, Jinliang Shi, Kan Luo, Zuojin Li, Lu Liao, Yingxu Wang
Copyright: © 2016 |Volume: 10 |Issue: 4 |Pages: 16
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781466689671|DOI: 10.4018/IJCINI.2016100104
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MLA

Bai, Junjie, et al. "Dimensional Music Emotion Recognition by Machine Learning." IJCINI vol.10, no.4 2016: pp.74-89. http://doi.org/10.4018/IJCINI.2016100104

APA

Bai, J., Feng, L., Peng, J., Shi, J., Luo, K., Li, Z., Liao, L., & Wang, Y. (2016). Dimensional Music Emotion Recognition by Machine Learning. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 10(4), 74-89. http://doi.org/10.4018/IJCINI.2016100104

Chicago

Bai, Junjie, et al. "Dimensional Music Emotion Recognition by Machine Learning," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 10, no.4: 74-89. http://doi.org/10.4018/IJCINI.2016100104

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Abstract

Music emotion recognition (MER) is a challenging field of studies that has been addressed in multiple disciplines such as cognitive science, physiology, psychology, musicology, and arts. In this paper, music emotions are modeled as a set of continuous variables composed of valence and arousal (VA) values based on the Valence-Arousal model. MER is formulated as a regression problem where 548 dimensions of music features were extracted and selected. A wide range of methods including multivariate adaptive regression spline, support vector regression (SVR), radial basis function, random forest regression (RFR), and regression neural networks are adopted to recognize music emotions. Experimental results show that these regression algorithms have led to good regression effect for MER. The optimal R2 statistics and VA values are 29.3% and 62.5%, respectively, which are obtained by the RFR and SVR algorithms in the relief feature space.

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