Flexible ICA solution for nonlinear blind source separation problem

Presented is a new architecture and a new learning algorithm that are exploited to resolve the blind source separation problem under stricter constraints than those considered to date. The mixing model that is assumed is an evolution of the well-known post-nonlinear (PNL) one: the PNL mixing block is followed by a convolutive mixing channel. The flexibility of the algorithm originates from the spline-SG neurons performing an on-line estimation of the score functions.