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Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies

Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies

Junjie Bai, Kan Luo, Jun Peng, Jinliang Shi, Ying Wu, Lixiao Feng, Jianqing Li, Yingxu Wang
Copyright: © 2017 |Volume: 11 |Issue: 4 |Pages: 13
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522511724|DOI: 10.4018/IJCINI.2017100105
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MLA

Bai, Junjie, et al. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies." IJCINI vol.11, no.4 2017: pp.80-92. http://doi.org/10.4018/IJCINI.2017100105

APA

Bai, J., Luo, K., Peng, J., Shi, J., Wu, Y., Feng, L., Li, J., & Wang, Y. (2017). Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 11(4), 80-92. http://doi.org/10.4018/IJCINI.2017100105

Chicago

Bai, Junjie, et al. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 11, no.4: 80-92. http://doi.org/10.4018/IJCINI.2017100105

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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.

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