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Classification of acoustic events using SVM-based clustering schemes
- Source :
-
Pattern Recognition . Apr2006, Vol. 39 Issue 4, p682-694. 13p. - Publication Year :
- 2006
-
Abstract
- Abstract: Acoustic events produced in controlled environments may carry information useful for perceptually aware interfaces. In this paper we focus on the problem of classifying 16 types of meeting-room acoustic events. First of all, we have defined the events and gathered a sound database. Then, several classifiers based on support vector machines (SVM) are developed using confusion matrix based clustering schemes to deal with the multi-class problem. Also, several sets of acoustic features are defined and used in the classification tests. In the experiments, the developed SVM-based classifiers are compared with an already reported binary tree scheme and with their correlative Gaussian mixture model (GMM) classifiers. The best results are obtained with a tree SVM-based classifier that may use a different feature set at each node. With it, a 31.5% relative average error reduction is obtained with respect to the best result from a conventional binary tree scheme. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 39
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 19704806
- Full Text :
- https://doi.org/10.1016/j.patcog.2005.11.005