Dimensional music emotion recognition by valence-arousal regression
J Bai, J Peng, J Shi, D Tang, Y Wu… - 2016 IEEE 15th …, 2016 - ieeexplore.ieee.org
As hot topics in current research, music emotion recognition (MER) have been addressed by
different disciplines such as physiology, psychology, musicology, cognitive science, etc. In
this paper, music emotions was modeled as continuous variables composed of valence and
arousal values (VA values) based on Valence-Arousal model, and MER is formulated as a
regression problem. 548 dimensions of music features were extracted and selected. The
support vector regression, random forest regression and regression neural networks were …
different disciplines such as physiology, psychology, musicology, cognitive science, etc. In
this paper, music emotions was modeled as continuous variables composed of valence and
arousal values (VA values) based on Valence-Arousal model, and MER is formulated as a
regression problem. 548 dimensions of music features were extracted and selected. The
support vector regression, random forest regression and regression neural networks were …
[CITATION][C] Dimensional Music Emotion Recognition by Valence-Arousal Regression (pp. 42-49)
J Bai, P Jun, S Jinliang, T Dedong, W Ying, L Jianqing… - Proceedings of 2016 IEEE …, 2016
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