A new sparsity-enabled signal separation method based on signal resonance

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

Abstract

This paper proposes the separation of signal components based on resonance. The method relies on several recent developments in sparse signal processing: morphological component analysis (MCA), the rational-dilation wavelet transform (RADWT), and fast algorithms for ℓ1-norm regularized linear inverse problems (for example, SALSA). The RADWT allows one to extract signal components according to resonance characteristics because the RADWT allows the Q-factor (frequency resolution) of the wavelet transform to be varied. The sought decomposition can not be accomplished by frequency-based filtering. An example illustrates the method.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages4150-4153
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

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

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Morphological component analysis
  • Q-factor
  • Sparse signal representation
  • Wavelets

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A new sparsity-enabled signal separation method based on signal resonance'. Together they form a unique fingerprint.

  • Cite this

    Selesnick, I. W. (2010). A new sparsity-enabled signal separation method based on signal resonance. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 4150-4153). [5495719] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2010.5495719