Journal
SIGNAL PROCESSING
Volume 91, Issue 12, Pages 2793-2809Publisher
ELSEVIER
DOI: 10.1016/j.sigpro.2010.10.018
Keywords
Sparse signal representation; Constant-Q transform; Wavelet transform; Morphological component analysis
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Funding
- NSF [CCF-1018020]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1018020] Funding Source: National Science Foundation
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Numerous signals arising from physiological and physical processes, in addition to being non-stationary, are moreover a mixture of sustained oscillations and non-oscillatory transients that are difficult to disentangle by linear methods. Examples of such signals include speech, biomedical, and geophysical signals. Therefore, this paper describes a new nonlinear signal analysis method based on signal resonance, rather than on frequency or scale, as provided by the Fourier and wavelet transforms. This method expresses a signal as the sum of a 'high-resonance' and a 'low-resonance' component a high-resonance component being a signal consisting of multiple simultaneous sustained oscillations; a low-resonance component being a signal consisting of non-oscillatory transients of unspecified shape and duration. The resonance-based signal decomposition algorithm presented in this paper utilizes sparse signal representations, morphological component analysis, and constant-Q (wavelet) transforms with adjustable Q-factor. (C) 2010 Elsevier B.V. All rights reserved.
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