Orthogonal least square based support vector machine for the classification of infant cry with asphyxia

Orthogonal least square based support vector machine for the classification of infant cry with asphyxia

This paper describes the classification of asphyxiated infant cry using orthogonal least square (OLS) based Support vector machine (SVM). The features of the cry signal were extracted using mel frequency cepstral coefficient analysis and significant features were selected using OLS. SVM with linear...

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Published in:Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
Main Author: Sahak R.; Mansor W.; Lee Y.K.; Mohd Yassin A.I.; Zabidi A.
Format: Conference paper
Language:English
Published: 2010
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650670629&doi=10.1109%2fBMEI.2010.5639300&partnerID=40&md5=4b567b7c2423b9bce1c7ef9ce9270695
id 2-s2.0-78650670629
spelling 2-s2.0-78650670629
Sahak R.; Mansor W.; Lee Y.K.; Mohd Yassin A.I.; Zabidi A.
Orthogonal least square based support vector machine for the classification of infant cry with asphyxia
2010
Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
3

10.1109/BMEI.2010.5639300
https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650670629&doi=10.1109%2fBMEI.2010.5639300&partnerID=40&md5=4b567b7c2423b9bce1c7ef9ce9270695
This paper describes the classification of asphyxiated infant cry using orthogonal least square (OLS) based Support vector machine (SVM). The features of the cry signal were extracted using mel frequency cepstral coefficient analysis and significant features were selected using OLS. SVM with linear and RBF kernels were used to classify the asphyxiated infant cry signals. Classification accuracy and support vector number were computed to examine the performance of the OLS based SVM. The highest classification accuracy (93.16%) could be achieved using RBF kernel, however, with large support vector number. ©2010 IEEE.


English
Conference paper
author Sahak R.; Mansor W.; Lee Y.K.; Mohd Yassin A.I.; Zabidi A.
spellingShingle Sahak R.; Mansor W.; Lee Y.K.; Mohd Yassin A.I.; Zabidi A.
Orthogonal least square based support vector machine for the classification of infant cry with asphyxia
author_facet Sahak R.; Mansor W.; Lee Y.K.; Mohd Yassin A.I.; Zabidi A.
author_sort Sahak R.; Mansor W.; Lee Y.K.; Mohd Yassin A.I.; Zabidi A.
title Orthogonal least square based support vector machine for the classification of infant cry with asphyxia
title_short Orthogonal least square based support vector machine for the classification of infant cry with asphyxia
title_full Orthogonal least square based support vector machine for the classification of infant cry with asphyxia
title_fullStr Orthogonal least square based support vector machine for the classification of infant cry with asphyxia
title_full_unstemmed Orthogonal least square based support vector machine for the classification of infant cry with asphyxia
title_sort Orthogonal least square based support vector machine for the classification of infant cry with asphyxia
publishDate 2010
container_title Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010
container_volume 3
container_issue
doi_str_mv 10.1109/BMEI.2010.5639300
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650670629&doi=10.1109%2fBMEI.2010.5639300&partnerID=40&md5=4b567b7c2423b9bce1c7ef9ce9270695
description This paper describes the classification of asphyxiated infant cry using orthogonal least square (OLS) based Support vector machine (SVM). The features of the cry signal were extracted using mel frequency cepstral coefficient analysis and significant features were selected using OLS. SVM with linear and RBF kernels were used to classify the asphyxiated infant cry signals. Classification accuracy and support vector number were computed to examine the performance of the OLS based SVM. The highest classification accuracy (93.16%) could be achieved using RBF kernel, however, with large support vector number. ©2010 IEEE.
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issn
language English
format Conference paper
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