Application of Bhattacharyya Kernel-based centroid neural network to the classification of audio signals

Authors:
Jae-Young Kim;Dong-Chul Park
Affiliations:
Department of Information Engineering, Center for Intel. Imaging Sys. Research, Myong Ji University, YongIn, Korea;Department of Information Engineering, Center for Intel. Imaging Sys. Research, Myong Ji University, YongIn, Korea
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

A novel approach for the classification of audio signals using Centroid Neural Network with Bhattacharyya kernel (CNNIBK) is evaluated and reported in this paper. The 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. Extensive experiments and results on a set of audio data demonstrate that the classification scheme based on CNNIBK outperforms CNN and Self-Organizing Map(SOM) that utilize Euclidean distance for their distance measure in terms of classification accuracy.