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Multiridge detection and time-frequency reconstruction
Open accessAbstract
The ridges of the wavelet transform, the Gabor transform, or any time-frequency representation of a signal contain crucial information on the characteristics of the signal. Indeed, they mark the regions of the time-frequency plane where the signal concentrates most of its energy. We introduce a new algorithm to detect and identify these ridges. The procedure is based on an original form of Markov chain Monte Carlo algorithm especially adapted to the present situation. We show that this detection algorithm is especially useful for noisy signals with multiridge transforms. It is a common practice among practitioners to reconstruct a signal from the skeleton of a transform of the signal (i.e., the restriction of the transform to the ridges). After reviewing several known procedures, we introduce a new reconstruction algorithm, and we illustrate its efficiency on speech signals and its robustness and stability on chirps perturbed by synthetic noise at different SNRs.
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“The time–frequency ridges of a signal contain crucial information about the signal characteristics.”
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Metadata
- Authors
- Carmona, R. A., Hwang, W. L., & Torresani, B.
- Affiliations
- Princeton University (US)
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1999
- Volume
- 47
- Issue
- 2
- Pages
- 480-492
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2137508792
- Cited by
- 269
- References
- 17
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 18 · 2023: 19 · 2022: 18 · 2021: 16 · 2020: 12 · 2019: 15 · 2018: 12 · 2017: 10 · 2016: 7 · 2015: 12 · 2014: 9 · 2013: 16 · 2012: 18
- FWCI
- 4.64
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Locations
- IEEE Transactions on Signal Processing (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- University of California, Irvine
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- ridge detection, time-frequency representation, wavelet transform, Gabor transform, Markov chain Monte Carlo, signal reconstruction, noisy signals, speech signals, CHIRPS, synthetic noise
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