The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications
We analyze the stability properties of the Synchrosqueezing transform, a time-frequency signal analysis method that can identify and extract oscillatory components with time-varying frequency and amplitude. We show that Synchrosqueezing is robust to bounded perturbations of the signal and to Gaussia...
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Published in | Signal processing Vol. 93; no. 5; pp. 1079 - 1094 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
01.05.2013
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | We analyze the stability properties of the Synchrosqueezing transform, a time-frequency signal analysis method that can identify and extract oscillatory components with time-varying frequency and amplitude. We show that Synchrosqueezing is robust to bounded perturbations of the signal and to Gaussian white noise. These results justify its applicability to noisy or nonuniformly sampled data that is ubiquitous in engineering and the natural sciences. We also describe a practical implementation of Synchrosqueezing and provide guidance on tuning its main parameters. As a case study in the geosciences, we examine characteristics of a key paleoclimate change in the last 2.5 million years, where Synchrosqueezing provides significantly improved insights.
► We study the stability of the Synchrosqueezing transform for spectral analysis. ► Synchrosqueezing is shown to be robust to bounded errors and Gaussian white noise. ► We describe a numerical implementation and compare it with other techniques. ► We apply Synchrosqueezing to proxies describing the evolution of the global climate. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0165-1684 1872-7557 |
DOI: | 10.1016/j.sigpro.2012.11.029 |