A combined classifier of cry units with new acoustic attributes

Authors:
Sergio Cano;Israel Suaste;Daniel Escobedo;Carlos A. Reyes-García;Taco Ekkel
Affiliations:
CENPIS, University of Oriente, Stgo de Cuba, Cuba;INAOE, Puebla, Mexico;CENPIS, University of Oriente, Stgo de Cuba, Cuba;INAOE, Puebla, Mexico;Faculty of Informatics, University of Twente, The Netherlands
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

Citing 1
Cited 3

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Abstract

The present work proposes a combined classifier of infant cry units that links in a single structure two focuses: a threshold-based classification and ANN-based classification. The threshold-based classifier considers 4 new acoustic features:stridor, melody, voicedness, shifts, that show properly their robustness in front of alterations of the acoustics of infant cry concerned with the presence of some diseases. In order to satisfy the automatic estimation their practical implementations are also considered. The ANN-based classifier consists in a feed-forward network using the method of Scale Gradient Conjugate (MSGC) as learning algorithm and the MFCCs as input vectors to the net. Each focus or classification stage gives in the exit one indicator (FN1 and FN2) that generates to the output a decision on two classes with gradation (normal, moderately-pathologic and pathologic). The results demonstrate the potentiality of these types of combined classifiers when the advantages of each focus in particular are properly emphasized