Publication:
Time–frequency analysis in infant cry classification using quadratic time frequency distributions

cris.author.scopus-author-id 55064391500
cris.author.scopus-author-id 26655536100
cris.author.scopus-author-id 57200576499
cris.author.scopus-author-id 57202423769
cris.author.scopus-author-id 59015450700
cris.author.scopus-author-id 58832613400
cris.author.scopus-author-id 26653958200
dc.contributor.author Saraswathy J.
dc.contributor.author Hariharan M.
dc.contributor.author Khairunizam W.
dc.contributor.author Sarojini J.
dc.contributor.author Thiyagar N.
dc.contributor.author Sazali Y.
dc.contributor.author Nisha S.
dc.date.accessioned 2025-01-13T07:42:53Z
dc.date.available 2025-01-13T07:42:53Z
dc.date.issued 2018-01-01
dc.description.abstract This paper presents a new investigation of time–frequency (t–f) based signal processing approach using quadratic time–frequency distributions (QTFDs) namely spectrogram (SPEC), Wigner–Ville distribution (WVD), Smoothed–Wigner Ville distribution (SWVD), Choi–William distribution (CWD) and modified B-distribution (MBD) for classification of infant cry signals. t–f approaches have proved as an efficient approach for applications involving the non stationary signals. In feature extraction, a cluster of t–f based features were extracted by extending the time-domain and frequency-domain features to the joint t–f domain from the generated t–f representation. Conventional features such as mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs) were also extracted in order to compare the effectiveness of the t–f methods. The efficacy of the extracted feature vectors was validated using probabilistic neural network (PNN) and general regression neural network (GRNN). The proposed methodology was implemented to classify different sets of binary classification problems of infant cry signals from different native. The best empirical result of above 90% was reported and revealed the good potential of t–f methods in the context of infant cry classification.
dc.identifier.doi 10.1016/j.bbe.2018.05.002
dc.identifier.scopus 2-s2.0-85048279530
dc.identifier.uri https://hdl.handle.net/20.500.14170/11804
dc.relation.funding Ministry of Education
dc.relation.grantno 9003-00485
dc.relation.ispartof Biocybernetics and Biomedical Engineering
dc.relation.ispartofseries Biocybernetics and Biomedical Engineering
dc.relation.issn 02085216
dc.subject Classification | Infant cry | Quadratic time–frequency distributions | Time–frequency analysis | t–f based feature extraction
dc.title Time–frequency analysis in infant cry classification using quadratic time frequency distributions
dc.type Journal
dspace.entity.type Publication
oaire.citation.endPage 645
oaire.citation.issue 3
oaire.citation.startPage 634
oaire.citation.volume 38
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit SRM Institute of Science and Technology
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Hospital Sultanah Bahiyah
oairecerif.affiliation.orgunit Universiti Kuala Lumpur
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
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person.identifier.scopus-author-id 55064391500
person.identifier.scopus-author-id 26655536100
person.identifier.scopus-author-id 57200576499
person.identifier.scopus-author-id 57202423769
person.identifier.scopus-author-id 59015450700
person.identifier.scopus-author-id 58832613400
person.identifier.scopus-author-id 26653958200
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