The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications
Open accessAbstract
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. Highlights? 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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Cited by
Gilles, J. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2265222 · Cited by 2,210
Time-Frequency Reassignment and Synchrosqueezing: An Overview
Auger, F., Flandrin, P., Lin, Y.-T., McLaughlin, S., Meignen, S., Oberlin, T., & Wu, H.-T. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2265316 · Cited by 630
Self-citationOberlin, T., Meignen, S., & Perrier, V. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2391077 · Cited by 531
Recent progress of chatter prediction, detection and suppression in milling
Zhu, L., & Liu, C. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106840 · Cited by 341
Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty
Peng, W., Ye, Z.-S., & Chen, N. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2907440 · Cited by 294
Metadata
- Authors
- Thakur, G., Brevdo, E., Fučkar, N. S., & Wu, H.-T.
- Venue
- Signal Processing
- Publisher
- Elsevier BV
- Published
- 2013-05
- Volume
- 93
- Issue
- 5
- Pages
- 1079-1094
- ISSN
- 0165-1684 (ISSN-L)
- OpenAlex
- W2081986197
- Cited by
- 526
- References
- 36
- FWCI
- 38.10
- Type
- journal article
- Language
- en
- Open access
- green
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- Thakur, G., Brevdo, E., Fučkar, N. S., & Wu, H.-T. (2013). The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing. https://doi.org/10.1016/j.sigpro.2012.11.029
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