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Speech/Music Classification using SVM

by R. Thiruvengatanadhan, P. Dhanalakshmi, P. Suresh Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 6
Year of Publication: 2013
Authors: R. Thiruvengatanadhan, P. Dhanalakshmi, P. Suresh Kumar
10.5120/10931-5875

R. Thiruvengatanadhan, P. Dhanalakshmi, P. Suresh Kumar . Speech/Music Classification using SVM. International Journal of Computer Applications. 65, 6 ( March 2013), 36-41. DOI=10.5120/10931-5875

@article{ 10.5120/10931-5875,
author = { R. Thiruvengatanadhan, P. Dhanalakshmi, P. Suresh Kumar },
title = { Speech/Music Classification using SVM },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 6 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number6/10931-5875/ },
doi = { 10.5120/10931-5875 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:18:03.698456+05:30
%A R. Thiruvengatanadhan
%A P. Dhanalakshmi
%A P. Suresh Kumar
%T Speech/Music Classification using SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 6
%P 36-41
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Audio classification serves as the fundamental step towards the rapid growth in audio data volume. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work both, time domain and frequency domain features are extracted from the input signal. Time domain features are Zero Crossing Rate (ZCR) and Short Time Energy (STE). Frequency domain features are spectral centroid, spectral flux, spectral entropy and spectral roll-off. After feature extraction, classification is carried out, using SVM model. The proposed feature extraction and classification models results in better accuracy in speech/music classification.

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Index Terms

Computer Science
Information Sciences

Keywords

Audio classification Feature extraction Zero Crossing Rate(ZCR) Short Time Energy (STE) Spectral centroid Spectral flux Spectral entropy Spectral roll-off Support vector Machine (SVM)

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