Nonlinear Blind Source Separation for Sparse Sources - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2016

Nonlinear Blind Source Separation for Sparse Sources


—Blind Source Separation (BSS) is the problem of separating signals which are mixed through an unknown function from a number of observations, without any information about the mixing model. Although it has been mathematically proven that the separation can be done when the mixture is linear, there is not any proof for the separability of nonlinearly mixed signals. Our contribution in this paper is performing nonlinear BSS for sparse sources. It is shown in this case, sources are separable even if the problem is under-determined (the number of observations is less that the number of source signals). However in the most general case (when the nonlinear mixing model can be of any kind and there is no side-information about that), an unknown nonlinear transformation of each source is reconstructed. It is shown why the problem reconstructing the exact sources is severely ill-posed and impossible to do without any other information.
Fichier principal
Vignette du fichier
1570252167.pdf (272.26 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01400546 , version 1 (22-11-2016)


  • HAL Id : hal-01400546 , version 1


Bahram Ehsandoust, Bertrand Rivet, Christian Jutten, Massoud Babaie-Zadeh. Nonlinear Blind Source Separation for Sparse Sources. EUSIPCO 2016 - 24th European Signal Processing Conference, Aug 2016, Budapest, Hungary. ⟨hal-01400546⟩
517 View
465 Download


Gmail Facebook X LinkedIn More