Title :
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
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;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495719
