The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network

The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network

Artificial Neural Network has been widely applied for solving pattern recognition problems including infant cry classification for detecting infant health and physical status. Feature extraction is usually performed using Mel Frequency Cepstrum Coefficient (MFCC) analysis. If irrelevant features in...

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Published in:Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
Main Author: Zabidi A.; Mansor W.; Khuan L.Y.; Yassin I.M.; Sahak R.
Format: Conference paper
Language:English
Published: 2010
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955421687&doi=10.1109%2fIECBES.2010.5742213&partnerID=40&md5=aad245151b4af2b9f4753e885cdabbe8
id 2-s2.0-79955421687
spelling 2-s2.0-79955421687
Zabidi A.; Mansor W.; Khuan L.Y.; Yassin I.M.; Sahak R.
The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network
2010
Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010


10.1109/IECBES.2010.5742213
https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955421687&doi=10.1109%2fIECBES.2010.5742213&partnerID=40&md5=aad245151b4af2b9f4753e885cdabbe8
Artificial Neural Network has been widely applied for solving pattern recognition problems including infant cry classification for detecting infant health and physical status. Feature extraction is usually performed using Mel Frequency Cepstrum Coefficient (MFCC) analysis. If irrelevant features in the MFCC are not removed, the performance of the MLP will be degraded. The use of F-ratio is essential to select the significant features. This paper examines the effect of selecting features using F-ratio on the classification accuracy of the MLP. Results obtained from direct selection of coefficients and selection of coefficients via F-ratio, were compared. It is found that the contribution of F-ratio in the selection of input for the MLP has managed to produce high classification accuracy. © 2010 IEEE.


English
Conference paper
author Zabidi A.; Mansor W.; Khuan L.Y.; Yassin I.M.; Sahak R.
spellingShingle Zabidi A.; Mansor W.; Khuan L.Y.; Yassin I.M.; Sahak R.
The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network
author_facet Zabidi A.; Mansor W.; Khuan L.Y.; Yassin I.M.; Sahak R.
author_sort Zabidi A.; Mansor W.; Khuan L.Y.; Yassin I.M.; Sahak R.
title The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network
title_short The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network
title_full The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network
title_fullStr The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network
title_full_unstemmed The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network
title_sort The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network
publishDate 2010
container_title Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
container_volume
container_issue
doi_str_mv 10.1109/IECBES.2010.5742213
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955421687&doi=10.1109%2fIECBES.2010.5742213&partnerID=40&md5=aad245151b4af2b9f4753e885cdabbe8
description Artificial Neural Network has been widely applied for solving pattern recognition problems including infant cry classification for detecting infant health and physical status. Feature extraction is usually performed using Mel Frequency Cepstrum Coefficient (MFCC) analysis. If irrelevant features in the MFCC are not removed, the performance of the MLP will be degraded. The use of F-ratio is essential to select the significant features. This paper examines the effect of selecting features using F-ratio on the classification accuracy of the MLP. Results obtained from direct selection of coefficients and selection of coefficients via F-ratio, were compared. It is found that the contribution of F-ratio in the selection of input for the MLP has managed to produce high classification accuracy. © 2010 IEEE.
publisher
issn
language English
format Conference paper
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