Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies

Junjie Bai, Kan Luo, Jun Peng, Jinliang Shi, Ying Wu, Lixiao Feng, Jianqing Li and Yingxu Wang
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Junjie Bai: School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China & Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
Kan Luo: School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China
Jun Peng: School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China
Jinliang Shi: School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China
Ying Wu: School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China
Lixiao Feng: School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China & Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
Jianqing Li: School of Instrument Science and Engineering, Southeast University, Nanjing, China
Yingxu Wang: International Institute of Cognitive Informatics and Cognitive Computing (ICIC),Laboratory for Computational Intelligence, Denotational Mathematics, and Software Science, Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada & Information Systems Lab, Stanford University, Stanford, CA, USA

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2017, vol. 11, issue 4, 80-92

Abstract: Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

Date: 2017
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