Presentation 2006/9/20
A Sparse Representation for Periodic Signal Mixtures
Makoto NAKASHIZUKA,
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Abstract(in English) This paper presents a method to decompose a signal into a set of periodic signals. The proposed method decomposes a signal while minimizing the weighted sum of l_2 norms of the periodic signals that are obtained by the decomposition to improve sparsity of the decomposition result. The proposed method is based on basis pursuit that decomposes a signal by adding l_1 norm penalty on the coefficients for the basis function to achieve the signal decomposition with fewer significant coefficients. In our approach, the weighted sum of the l_2 norm of the periodic signals instead of l_1 norm is minimized to decompose signal into fewer periods. We convert this decomposition problem to an unconstrained minimization problem as well as basis pursuit denoising. To solve this unconstrained minimization, we employ a relaxation algorithm that is based on the blcok coordinates relaxation(BCR) algorithm that has been applied to basis pursuit denoising. In experiment, comparisons between the periodicity transform (W. Sethares, 1999) and the proposed decomposition are shown. Moreover, the decomposition and signal separation of a speech mixture are also demonstrated.
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Keyword(in English) Periodic signals / period estimation / harmonics / signal approximation / signal decomposition / basis pursuit
Paper # SIP2006-78,SIS2006-41,SP2006-64
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Committee SIS
Conference Date 2006/9/20(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) A Sparse Representation for Periodic Signal Mixtures
Sub Title (in English)
Keyword(1) Periodic signals
Keyword(2) period estimation
Keyword(3) harmonics
Keyword(4) signal approximation
Keyword(5) signal decomposition
Keyword(6) basis pursuit
1st Author's Name Makoto NAKASHIZUKA
1st Author's Affiliation Graduate School of Engineering Science, Osaka University()
Date 2006/9/20
Paper # SIP2006-78,SIS2006-41,SP2006-64
Volume (vol) vol.106
Number (no) 262
Page pp.pp.-
#Pages 6
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