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Automatic Music Mood Recognition using Support Vector Regression

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Manisha Sarode, D. G. Bhalke

Manisha Sarode and D G Bhalke. Automatic Music Mood Recognition using Support Vector Regression. International Journal of Computer Applications 163(5):32-35, April 2017. BibTeX

	author = {Manisha Sarode and D. G. Bhalke},
	title = {Automatic Music Mood Recognition using Support Vector Regression},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2017},
	volume = {163},
	number = {5},
	month = {Apr},
	year = {2017},
	issn = {0975-8887},
	pages = {32-35},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017913533},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Music is a dialect of feelings, and henceforth music feeling could be helpful in music understanding, proposal, recovery and some other music-related applications. Numerous issues for music feeling acknowledgment have been tended to by various teaches, for example, physiology, brain science, intellectual science and musicology. Music emotion regression is considered more appropriate than classification for music emotion retrieval, since it resolves some of the ambiguities of emotion classes. We present a music emotion recognition system based on support vector regression (SVR) method. The process of recognition consists of three steps: (i) Several music features have been extracted from music signal; (ii) those features have been mapped into various emotion categories on Thayer’s two-dimensional emotion model; (iii) two regression functions have been trained using SVR and then arousal and valence values are predicted.


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Arousal, Valence, music emotion recognition (MER), support vector regression.