Publication:
An accurate infant cry classification system based on continuos hidden Markov model

Simple item page
dc.citedby10
dc.contributor.authorAbdulaziz Y.en_US
dc.contributor.authorAhmad S.M.S.en_US
dc.contributor.authorid57207857499en_US
dc.contributor.authorid24721182400en_US
dc.date.accessioned2023-12-28T07:17:49Z
dc.date.available2023-12-28T07:17:49Z
dc.date.issued2010
dc.description.abstractThis paper describes the feasibility study of applying a novel continuous Hidden Markov Model algorithm as a classifier to an automatic infant cry classification system which main task is to classify and differentiate between pain and non-pain cries belonging to infants. The classification system is trained based on Baum -Welch algorithm on a pair of local feature vectors. In this study, Mel Frequency Cepstral Coefficient (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) are extracted from the audio samples of infant's cries and are fed into the classification module. The system accuracy reported in this study varies from 71.8% up to 92.3% under different parameter settings, whereby in general the system that are bases on MFCC features performs better than the one that utilizes LPCC features. The encouraging results demonstrate that indeed Hidden Markov Model provides for a robust and accurate infant cry classification system. � 2010 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5561472
dc.identifier.doi10.1109/ITSIM.2010.5561472
dc.identifier.epage1652
dc.identifier.scopus2-s2.0-78049373758
dc.identifier.spage1648
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-78049373758&doi=10.1109%2fITSIM.2010.5561472&partnerID=40&md5=a076128513395701d0453dfbbd616cc9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29632
dc.identifier.volume3
dc.pagecount4
dc.sourceScopus
dc.sourcetitleProceedings 2010 International Symposium on Information Technology - System Development and Application and Knowledge Society, ITSim'10
dc.subjectContinuos hidden Markov model
dc.subjectInfant pain cry classification
dc.subjectLinear prediction cepstral coefficints
dc.subjectMel frequency cepstral coefficient
dc.subjectExtraction
dc.subjectForecasting
dc.subjectHealth
dc.subjectInformation technology
dc.subjectSpeech recognition
dc.subjectAudio samples
dc.subjectBaum-Welch algorithms
dc.subjectClassification system
dc.subjectContinuos hidden Markov model
dc.subjectContinuous hidden Markov model
dc.subjectFeasibility studies
dc.subjectInfant cry
dc.subjectInfant pain cry classification
dc.subjectLinear prediction
dc.subjectLinear prediction cepstral coefficients
dc.subjectLocal feature vectors
dc.subjectMain tasks
dc.subjectMel-frequency cepstral coefficients
dc.subjectParameter setting
dc.subjectSystem accuracy
dc.subjectHidden Markov models
dc.titleAn accurate infant cry classification system based on continuos hidden Markov modelen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS