Dynamic spectrum classification by divergence-based kernel machines and its application to the detection of worn-out banknotes

Tsukasa Ishigaki, Tomoyuki Higuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In the kernel method, the appropriate selection or design of the kernel function is important for the construction of a high-performance classifier. The present paper describes a dynamic spectrum classification method using kernel classifiers with the divergence-based kernel and its application to the detection of worn-out banknotes. We introduce the divergence-based kernel that was proposed as a measure between two probability distributions into the dynamic spectrum classification. The present method is applied to the detection of worn-out banknotes by using acoustic signals for the facilitation of identifying counterfeit banknotes. As a result, the classification performance using the divergence-based kernel is shown to have better performance than those using common kernels such as the Gaussian kernel or the polynomial kernel.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages1873-1876
Number of pages4
DOIs
Publication statusPublished - 2008 Sep 16
Externally publishedYes
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: 2008 Mar 312008 Apr 4

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period08/3/3108/4/4

Keywords

  • Acoustic applications
  • Acoustic signal processing
  • Kernel method
  • Pattern recognition
  • Spectrum classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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