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dc.contributor.authorHariharan, Muthusamy
dc.contributor.authorLim, Sin Chee
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.date.accessioned2012-05-10T13:03:43Z
dc.date.available2012-05-10T13:03:43Z
dc.date.issued2012
dc.identifier.citationJournal of Medical Systems, vol. 36 (3), 2012, pages 1309-1315en_US
dc.identifier.issn0148-5598
dc.identifier.urihttp://www.springerlink.com/content/057417061p17u2x5/
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/19123
dc.descriptionLink to publisher's homepage at http://www.springerlink.com/en_US
dc.description.abstractAcoustic 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.en_US
dc.language.isoenen_US
dc.publisherSpringer Science+Business Media, LLC.en_US
dc.subjectAcoustic analysisen_US
dc.subjectInfant cryen_US
dc.subjectWeighted LPCCsen_US
dc.subjectProbabilistic neural networken_US
dc.titleAnalysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural networken_US
dc.typeArticleen_US
dc.contributor.urlhari@unimap.edu.myen_US
dc.contributor.urlsclim3@gmail.comen_US
dc.contributor.urlsyaacob@unimap.edu.myen_US


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