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Time-Scale Synthesis for Locally Stationary Signals

Abstract : We develop a time-scale synthesis-based probabilistic approach for the modeling of locally stationary signals. Inspired by our previous work, the model involves zero-mean, complex Gaussian wavelet coefficients, whose distribution varies as a function of time by time dependent translations on the scale axis. In a maximum a posteriori approach, we propose an estimator for the model parameters, namely the time-varying scale translation and an underlying power spectrum. The proposed approach is illustrated on a denoising example. It is also shown that the model can handle locally stationary signals with fast frequency variations, and provide in this case very sharp timescale representations more concentrated than synchrosqueezed or reassigned wavelet transform.
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https://hal.archives-ouvertes.fr/hal-02469842
Contributor : Bruno Torrésani <>
Submitted on : Thursday, February 6, 2020 - 5:54:00 PM
Last modification on : Thursday, April 16, 2020 - 4:57:34 PM
Long-term archiving on: : Thursday, May 7, 2020 - 4:24:33 PM

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Adrien Meynard, Bruno Torrésani. Time-Scale Synthesis for Locally Stationary Signals. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, Barcelona, Spain. pp.5820-5824, ⟨10.1109/ICASSP40776.2020.9053069⟩. ⟨hal-02469842⟩

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