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Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network

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Main Authors: Hariharan, Muthusamy, Lim, Sin Chee, Sazali, Yaacob, Prof. Dr.
Other Authors: hari@unimap.edu.my
Format: Article
Language:English
Published: Springer Science+Business Media, LLC. 2012
Subjects:
Acoustic analysis
Infant cry
Weighted LPCCs
Probabilistic neural network
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/19123
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spelling my.unimap-191232012-05-10T13:03:43Z Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network Hariharan, Muthusamy Lim, Sin Chee Sazali, Yaacob, Prof. Dr. hari@unimap.edu.my sclim3@gmail.com syaacob@unimap.edu.my Acoustic analysis Infant cry Weighted LPCCs Probabilistic neural network Link to publisher's homepage at http://www.springerlink.com/ Acoustic analysis of infant cry signals has been proven to be an excellent tool in the area of automatic detection of pathological status of an infant. This paper investigates the application of parameter weighting for linear prediction cepstral coefficients (LPCCs) to provide the robust representation of infant cry signals. Three classes of infant cry signals were considered such as normal cry signals, cry signals from deaf babies and babies with asphyxia. A Probabilistic Neural Network (PNN) is suggested to classify the infant cry signals into normal and pathological cries. PNN is trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the suggested features and classification algorithms give very promising classification accuracy of above 98% and it expounds that the suggested method can be used to help medical professionals for diagnosing pathological status of an infant from cry signals. 2012-05-10T13:03:43Z 2012-05-10T13:03:43Z 2012 Article Journal of Medical Systems, vol. 36 (3), 2012, pages 1309-1315 0148-5598 http://www.springerlink.com/content/057417061p17u2x5/ http://hdl.handle.net/123456789/19123 en Springer Science+Business Media, LLC.
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 Acoustic analysis
Infant cry
Weighted LPCCs
Probabilistic neural network
spellingShingle Acoustic analysis
Infant cry
Weighted LPCCs
Probabilistic neural network
Hariharan, Muthusamy
Lim, Sin Chee
Sazali, Yaacob, Prof. Dr.
Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network
description Link to publisher's homepage at http://www.springerlink.com/
author2 hari@unimap.edu.my
author_facet hari@unimap.edu.my
Hariharan, Muthusamy
Lim, Sin Chee
Sazali, Yaacob, Prof. Dr.
format Article
author Hariharan, Muthusamy
Lim, Sin Chee
Sazali, Yaacob, Prof. Dr.
author_sort Hariharan, Muthusamy
title Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network
title_short Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network
title_full Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network
title_fullStr Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network
title_full_unstemmed Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network
title_sort analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network
publisher Springer Science+Business Media, LLC.
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/19123
_version_ 1643789823147769856
score 13.187159

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