Document Detail

Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network.
MedLine Citation:
PMID:  20844933     Owner:  NLM     Status:  In-Data-Review    
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.
M Hariharan; Lim Sin Chee; Sazali Yaacob
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Publication Detail:
Type:  Journal Article     Date:  2010-09-16
Journal Detail:
Title:  Journal of medical systems     Volume:  36     ISSN:  0148-5598     ISO Abbreviation:  J Med Syst     Publication Date:  2012 Jun 
Date Detail:
Created Date:  2012-05-07     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7806056     Medline TA:  J Med Syst     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1309-15     Citation Subset:  IM    
School of Mechatronic Engineering, UniMAP, Arau, Malaysia,
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