Transient acoustic signal classification using joint sparse representation

Haichao Zhang, Nasser M. Nasrabadi, Thomas S Huang, Yanning Zhang

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

Abstract

In this paper, we present a novel joint sparse representation based method for acoustic signal classification with multiple measurements. The proposed method exploits the correlations among the multiple measurements with the notion of joint sparsity for improving the classification accuracy. Extensive experiments are carried out on real acoustic data sets and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2220-2223
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

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

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • Joint sparsity classification
  • joint sparse recovery
  • sparse representation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Zhang, H., Nasrabadi, N. M., Huang, T. S., & Zhang, Y. (2011). Transient acoustic signal classification using joint sparse representation. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 2220-2223). [5946922] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5946922