Nonlinear blind source separation for sparse sources

Bahram Ehsandoust, Bertrand Rivet, Christian Jutten, Massoud Babaie-Zadeh
2016 2016 24th European Signal Processing Conference (EUSIPCO)  
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
more » ... f 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.
doi:10.1109/eusipco.2016.7760515 dblp:conf/eusipco/EhsandoustRJB16 fatcat:vgdfqwff6jastpg57kj2loz2fu