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Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks

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Main Authors: Muthusamy, Hariharan, Jeyaraman, Saraswathy, Sindhu, Ravindran, Wan Khairunizam, Wan Ahmad, Dr., Sazali, Yaacob, Prof. Dr.
Other Authors: hari@unimap.edu.my
Format: Article
Language:English
Published: Elsevier Ltd 2013
Subjects:
Feature extraction
Infant cry
Probabilistic Neural Network (PNN)
General Regression Neural Network
Short-time Fourier transform
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/26408
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spelling my.unimap-264082013-07-02T08:49:29Z Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks Muthusamy, Hariharan Jeyaraman, Saraswathy Sindhu, Ravindran Wan Khairunizam, Wan Ahmad, Dr. Sazali, Yaacob, Prof. Dr. hari@unimap.edu.my Feature extraction Infant cry Probabilistic Neural Network (PNN) General Regression Neural Network Short-time Fourier transform Link to publisher's homepage at http://www.elsevier.com/ A cry is the first verbal communication of infants and it is described as a loud, high-pitched sound made by infants in response to certain situations. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and it has been proven to be an excellent tool to investigate the pathological status of an infant. This paper proposes short-time Fourier transform (STFT) based time-frequency analysis of infant cry signals. Few statistical features are derived from the time-frequency plot of infant cry signals and used as features to quantify infant cry signals. Two types of radial basis neural networks such as Probabilistic Neural Network (PNN) and General Regression Neural Network are employed as classifiers for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals of infants with asphyxia. For comparison, the proposed features are also tested using two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm. The experimental results show that the PNN and GRNN give very promising classification accuracy compared to MLP and TDNN and the proposed methods can effectively classify normal and pathological infant cries of infants with asphyxia. 2013-07-02T08:49:29Z 2013-07-02T08:49:29Z 2012-08 Article Expert Systems with Applications, vol. 39(10), 2012, pages 9515-9523 0957-4174 http://www.sciencedirect.com/science/article/pii/S095741741200365X http://hdl.handle.net/123456789/26408 en Elsevier Ltd
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 Feature extraction
Infant cry
Probabilistic Neural Network (PNN)
General Regression Neural Network
Short-time Fourier transform
spellingShingle Feature extraction
Infant cry
Probabilistic Neural Network (PNN)
General Regression Neural Network
Short-time Fourier transform
Muthusamy, Hariharan
Jeyaraman, Saraswathy
Sindhu, Ravindran
Wan Khairunizam, Wan Ahmad, Dr.
Sazali, Yaacob, Prof. Dr.
Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
description Link to publisher's homepage at http://www.elsevier.com/
author2 hari@unimap.edu.my
author_facet hari@unimap.edu.my
Muthusamy, Hariharan
Jeyaraman, Saraswathy
Sindhu, Ravindran
Wan Khairunizam, Wan Ahmad, Dr.
Sazali, Yaacob, Prof. Dr.
format Article
author Muthusamy, Hariharan
Jeyaraman, Saraswathy
Sindhu, Ravindran
Wan Khairunizam, Wan Ahmad, Dr.
Sazali, Yaacob, Prof. Dr.
author_sort Muthusamy, Hariharan
title Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
title_short Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
title_full Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
title_fullStr Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
title_full_unstemmed Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
title_sort infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
publisher Elsevier Ltd
publishDate 2013
url http://dspace.unimap.edu.my/xmlui/handle/123456789/26408
_version_ 1643794934669508608
score 13.222552

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