Synthesis-based time-scale transforms for non-stationary signals - Ecole Centrale de Marseille Accéder directement au contenu
Article Dans Une Revue Applied and Computational Harmonic Analysis Année : 2023

Synthesis-based time-scale transforms for non-stationary signals

Adrien Meynard

Résumé

This paper deals with the modeling of non-stationary signals, from the point of view of signal synthesis. A class of random, non-stationary signals, generated by synthesis from a random timescale representation, is introduced and studied. Non-stationarity is implemented in the timescale representation through a prior distribution which models the action of time warping on a stationary signal. A main originality of the approach is that models directly a timescale representation from which signals can be synthesized, instead of post-processing a pre-computed timescale transform. A maximum a posteriori estimator is proposed for the time warping parameters and the power spectrum of an underlying stationary signal, together with an iterative algorithm, called JEFAS-S, for the estimation, based upon the Expectation Maximization approach. Numerical results show the ability of JEFAS-S to estimate accurately time warping and power spectrum. This is in particular true when time warping involves fast variations, where a similar approach called JEFAS, proposed earlier, fails. In addition, as a by-product, the approach is able to yield extremely sharp timescale representations, also in the case of fast varying non-stationarity, where standard approaches such as synchrosqueezing fail.
Fichier principal
Vignette du fichier
ACHA_MT_HAL.pdf (1.72 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03669280 , version 1 (16-05-2022)
hal-03669280 , version 2 (07-11-2022)

Identifiants

Citer

Adrien Meynard, Bruno Torrésani. Synthesis-based time-scale transforms for non-stationary signals. Applied and Computational Harmonic Analysis, 2023, 65, pp.112-136. ⟨10.1016/j.acha.2023.02.001⟩. ⟨hal-03669280v2⟩
186 Consultations
133 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More