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Audio event classification using binary hierarchical classifiers with feature selection for healthcare applications

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3 Author(s)
Ya-Ti Peng ; Dept. of Electr. Eng., Univ. of Washington, Seattle, WA ; Ching-Yung Lin ; Ming-Ting Sun

In this paper, a binary hierarchical classifier with feature selection is proposed for multi-class audio event classification for healthcare applications. We consider the hierarchical clustering and the feature selection problems jointly when building a binary hierarchical classifier. The proposed method results in the classifier structure as well as a compact feature subset for each component classifier for constructing the overall binary hierarchical classifier. With Support Vector Machine (SVM) for the component classifiers in our experiment, results from classifying several key audio events for the eldercare application show competitive performance to the traditional one-against-one method while the number of training and testing SVM is less in our proposed scheme. Moreover, feature selection facilitates the training of the component classifier by filtering out possible redundant and irrelevant feature components.

Published in:

Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on

Date of Conference:

18-21 May 2008