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Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network

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Main Authors: Hariharan, Muthusamy, Dr., Sindhu, R, Sazali, Yaacob, Prof. Dr.
Other Authors: hari@unimap.edu.my
Format: Article
Language:English
Published: Elsevier Ireland Ltd. 2012
Subjects:
Acoustic analysis
Feature extraction
General Regression Neural Network
Infant cry
Pattern classification
Short-time Fourier transform
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21299
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spelling my.unimap-212992012-10-11T02:26:02Z Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network Hariharan, Muthusamy, Dr. Sindhu, R Sazali, Yaacob, Prof. Dr. hari@unimap.edu.my Acoustic analysis Feature extraction General Regression Neural Network Infant cry Pattern classification Short-time Fourier transform Link to publisher's homepage at http://www.elsevier.com/ Crying is the most noticeable behavior of infancy. 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. General Regression Neural Network (GRNN) is employed as a classifier for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals from deaf infants. To prove the reliability of the proposed features, two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm are also used as classifiers. The experimental results show that the GRNN classifier gives very promising classification accuracy compared to MLP and TDNN and the proposed method can effectively classify normal and pathological infant cries. 2012-10-11T02:26:02Z 2012-10-11T02:26:02Z 2012-11 Article Computer Methods and Programs in Biomedicine, vol., 108 (2), 2012, pages 559–569 0169-2607 http://www.sciencedirect.com/science/article/pii/S0169260711001982 http://hdl.handle.net/123456789/21299 en Elsevier Ireland 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 Acoustic analysis
Feature extraction
General Regression Neural Network
Infant cry
Pattern classification
Short-time Fourier transform
spellingShingle Acoustic analysis
Feature extraction
General Regression Neural Network
Infant cry
Pattern classification
Short-time Fourier transform
Hariharan, Muthusamy, Dr.
Sindhu, R
Sazali, Yaacob, Prof. Dr.
Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
description Link to publisher's homepage at http://www.elsevier.com/
author2 hari@unimap.edu.my
author_facet hari@unimap.edu.my
Hariharan, Muthusamy, Dr.
Sindhu, R
Sazali, Yaacob, Prof. Dr.
format Article
author Hariharan, Muthusamy, Dr.
Sindhu, R
Sazali, Yaacob, Prof. Dr.
author_sort Hariharan, Muthusamy, Dr.
title Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
title_short Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
title_full Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
title_fullStr Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
title_full_unstemmed Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
title_sort normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
publisher Elsevier Ireland Ltd.
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21299
_version_ 1643791096304631808
score 13.222552

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