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Automatic Music Mood Recognition using Support Vector Regression

by Manisha Sarode, D. G. Bhalke
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 5
Year of Publication: 2017
Authors: Manisha Sarode, D. G. Bhalke
10.5120/ijca2017913533

Manisha Sarode, D. G. Bhalke . Automatic Music Mood Recognition using Support Vector Regression. International Journal of Computer Applications. 163, 5 ( Apr 2017), 32-35. DOI=10.5120/ijca2017913533

@article{ 10.5120/ijca2017913533,
author = { Manisha Sarode, D. G. Bhalke },
title = { Automatic Music Mood Recognition using Support Vector Regression },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 5 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 32-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number5/27394-2017913533/ },
doi = { 10.5120/ijca2017913533 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:23.299831+05:30
%A Manisha Sarode
%A D. G. Bhalke
%T Automatic Music Mood Recognition using Support Vector Regression
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 5
%P 32-35
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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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Index Terms

Computer Science
Information Sciences

Keywords

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

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