Optimal features for classifying asphyxiated infant cry using support vector machine with RBF kernel

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Optimal features for classifying asphyxiated infant cry using support vector machine with RBF kernel

An investigation into optimizing the input feature set for classifier to identify infant cry signals with asphyxia is presented in this paper. Mel frequency cepstrum coefficients were used to represent the infant cry signals collected from the Instituto Nacional De Astrofisica Opticay Electronica, M...

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Bibliographic Details
Published in:	IFMBE Proceedings
Main Author:	Sahak R.; Lee Y.K.; Mansor W.; Yassin A.I.M.; Zabidi A.
Format:	Conference paper
Language:	English
Published:	2011
Online Access:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-80555148855&doi=10.1007%2f978-3-642-23508-5_27&partnerID=40&md5=1774b657ea76e43bdeef76c2d843e54f

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