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

Author

Selesnick, Ivan W.

Author_Institution

Electr. & Comput. Eng., Polytech. Inst. of New York Univ., Brooklyn, OH, USA

fYear

2010

fDate

14-19 March 2010

Firstpage

4150

Lastpage

4153

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.

Keywords

Q-factor; source separation; statistical analysis; wavelet transforms; Q-factor; fast algorithms; frequency resolution; morphological component analysis; rational-dilation wavelet transform; signal resonance; sparsity-enabled signal separation; Algorithm design and analysis; Frequency; Inverse problems; Q factor; Resonance; Signal analysis; Signal processing algorithms; Source separation; Wavelet analysis; Wavelet transforms; Q-factor; Sparse signal representation; morphological component analysis; wavelets;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Conference_Location

Dallas, TX

ISSN

1520-6149

Print_ISBN

978-1-4244-4295-9

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2010.5495719

Filename

5495719