A Multicomponent proximal algorithm for Empirical Mode Decomposition

Nelly Pustelnik (1) , Pierre Borgnat (1) , Patrick Flandrin (1)

1 Phys-ENS - Laboratoire de Physique de l'ENS Lyon

The Empirical Mode Decomposition (EMD) is known to be a powerful tool adapted to the decomposition of a signal into a collection of intrinsic mode functions (IMF). A key procedure in the extraction of the IMFs is the sifting process whose main drawback is to depend on the choice of an interpolation method and to have no clear convergence guarantees. We propose a convex optimization procedure in order to replace the sifting process in the EMD. The considered method is based on proximal tools, which allow us to deal with a large class of constraints such as quasi-orthogonality or extrema-based constraints.

Mots clés: EMD; Trend-fluctuation; Convex optimization; Proximal algorithms

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Nelly Pustelnik, Pierre Borgnat, Patrick Flandrin. A Multicomponent proximal algorithm for Empirical Mode Decomposition. European Signal Processing Conference (EUSIPCO), Aug 2012, Bucharest, Romania. pp.x+5. ⟨hal-00826824⟩