Classification of Audio Signals Using a Bhattacharyya Kernel-Based Centroid Neural Network

Authors:
Dong-Chul Park;Yunsik Lee;Dong-Min Woo
Affiliations:
Dept. of Information Engineering, Myong Ji University, Korea;Korea Electronics Technology Institute, Seongnam, Korea;Dept. of Information Engineering, Myong Ji University, Korea
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

A novel approach for the classification of audio signals using a Bhattacharyya Kernel-based Centroid Neural Network (BK-CNN) is proposed and presented in this paper. The proposed classifier is based on Centroid Neural Network (CNN) and also exploits advantages of the kernel method for mapping input data into a higher dimensional feature space. Furthermore, since the feature vectors of audio signals are modelled by Gaussian Probability Density Function (GPDF), the classification procedure is performed by considering Bhattacharyya distance as the distance measure of the proposed classifier. Experiments and results on various audio data sets demonstrate that the proposed classification scheme based on BK-CNN outperforms conventional algorithms including Self-Organizing Map(SOM) and CNN.