Dynamic spectrum classification by kernel classifiers with divergence-based kernels and its applications to acoustic signals Online publication date: Sun, 25-Jan-2009
by Tsukasa Ishigaki, Tomoyuki Higuchi
International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), Vol. 1, No. 2, 2009
Abstract: In the kernel method, appropriate selection of the kernel function is important for the construction of a high-performance classifier. The present paper describes a high-accuracy dynamic spectrum classification method using kernel classifiers with a divergence-based kernel. We introduce the divergence, which is a metric between two probability distributions, as a kernel function for similarity calculations of two dynamic spectra with appropriate statistical signal processing. The method is applied to two problems of acoustic signal classification: 1 identification of the condition of hazelnut shells using acoustic signals to maintain the quality and safety of the hazelnut product; 2 detection of worn-out banknotes by using acoustic signals to facilitate identification of counterfeit banknotes. In both applications, classification using the divergence-based kernel demonstrates better performance than classifications using popular kernels such as the Gaussian kernel or the polynomial kernel.
Online publication date: Sun, 25-Jan-2009
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