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Performance comparison of daubechies wavelet family in Infant cry classification

Proceeding of The 8th International Colloquium on Signal Processing and Its Applications (CSPA 2012) at Melaka, Malaysia from 23 March 2012 through 25 March 2012. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp?tag=1

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Bibliographic Details
Main Authors: Saraswathy, J, Hariharan, Muthusamy, Vijean, Vikneswaran, Sazali, Yaacob, Prof. Dr., Wan Khairunizam, Wan Ahmad, Dr.
Other Authors: wathy_87@ymail.com
Format: Working Paper
Language:English
Published: IEEE Conference Publications 2014
Subjects:
Infant cry
Wavelet packet transform
Probabilistic neural network
General regression neural network
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/33964
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spelling my.unimap-339642014-05-10T17:09:59Z Performance comparison of daubechies wavelet family in Infant cry classification Saraswathy, J Hariharan, Muthusamy Vijean, Vikneswaran Sazali, Yaacob, Prof. Dr. Wan Khairunizam, Wan Ahmad, Dr. wathy_87@ymail.com hari@unimap.edu.my vicky.86max@gmail.com s.yaacob@unimap.edu.my khairunizam@unimap.edu.my Infant cry Wavelet packet transform Probabilistic neural network General regression neural network Proceeding of The 8th International Colloquium on Signal Processing and Its Applications (CSPA 2012) at Melaka, Malaysia from 23 March 2012 through 25 March 2012. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp?tag=1 Infant cry is a non-stationary, loud, high-pitched signal made by infants in response to certain situations. This acoustic signal can be used to identify physical or psychology status of infant. The aim of this work is to compare the performance of Daubechies wavelet family in infant cry classification. The orders of db1, db3, db4, db6 and db10 are chosen randomly for this investigation. Infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are computed at different sub bands. Two different case studies such as, normal versus asphyxia and normal versus hypoacoustic are performed. Two different types of radial basis artificial neural networks namely, Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) are used to classify the infant cry signals. The results emphasized that the proposed features and classification algorithms can be used to aid the medical professionals for diagnosing pathological status of infant cry. 2014-04-23T08:37:20Z 2014-04-23T08:37:20Z 2012 Working Paper p. 451-455 978-146730961-5 http://dspace.unimap.edu.my:80/dspace/handle/123456789/33964 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06194767 10.1109/CSPA.2012.6194767 en IEEE Conference Publications
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Infant cry
Wavelet packet transform
Probabilistic neural network
General regression neural network
spellingShingle Infant cry
Wavelet packet transform
Probabilistic neural network
General regression neural network
Saraswathy, J
Hariharan, Muthusamy
Vijean, Vikneswaran
Sazali, Yaacob, Prof. Dr.
Wan Khairunizam, Wan Ahmad, Dr.
Performance comparison of daubechies wavelet family in Infant cry classification
description Proceeding of The 8th International Colloquium on Signal Processing and Its Applications (CSPA 2012) at Melaka, Malaysia from 23 March 2012 through 25 March 2012. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp?tag=1
author2 wathy_87@ymail.com
author_facet wathy_87@ymail.com
Saraswathy, J
Hariharan, Muthusamy
Vijean, Vikneswaran
Sazali, Yaacob, Prof. Dr.
Wan Khairunizam, Wan Ahmad, Dr.
format Working Paper
author Saraswathy, J
Hariharan, Muthusamy
Vijean, Vikneswaran
Sazali, Yaacob, Prof. Dr.
Wan Khairunizam, Wan Ahmad, Dr.
author_sort Saraswathy, J
title Performance comparison of daubechies wavelet family in Infant cry classification
title_short Performance comparison of daubechies wavelet family in Infant cry classification
title_full Performance comparison of daubechies wavelet family in Infant cry classification
title_fullStr Performance comparison of daubechies wavelet family in Infant cry classification
title_full_unstemmed Performance comparison of daubechies wavelet family in Infant cry classification
title_sort performance comparison of daubechies wavelet family in infant cry classification
publisher IEEE Conference Publications
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/33964
_version_ 1643797360898211840
score 13.18916

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