Binary particle swarm optimization and f-ratio for selection of features in the recognition of asphyxiated infant cry

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Binary particle swarm optimization and f-ratio for selection of features in the recognition of asphyxiated infant cry

In the infant cry classification for detecting pathological conditions using Artificial Neural Network, a common feature extraction technique employed is Mel Frequency Cepstrum Coefficient (MFCC) analysis due to its good representation properties. However, not all MFCC features are significant for c...

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Bibliographic Details
Published in:	IFMBE Proceedings
Main Author:	Zabidi A.; Mansor W.; Khuan L.Y.; Yassin I.M.; Sahak R.
Format:	Conference paper
Language:	English
Published:	2011
Online Access:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-80455162695&doi=10.1007%2f978-3-642-23508-5_18&partnerID=40&md5=74e45a52a79f9bdd6743e94583b2e69d

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