Nonlinear ICA solutions for convolutive mixing of PNL mixtures

This paper introduces an ICA approach to a nonlinear convolutive BSS problem. The mixing model considered here is an evolution of the post nonlinear one: it is the convolutive mixing of PNL mixture. The main aim of this paper is to enlarge the set of blind sources separation problems that can be approached by nonlinear ICA with some stricter mixing environments than the one just widely described in literature. The flexibility of the algorithm is given by the on line estimation of the score function performed by spline neurons.