Low complexity adaptive nonlinear function for blind signal separation

An adaptive nonlinear function for blind signal separation is presented. It is based on a spline approximation whose control points are adaptively changed using information maximization techniques. The monotonously increasing characteristic is obtained using suitable B-spline functions imposing simple constraints on its control points. In particular, the problem of adaptively maximizing the entropy of the output is considered in the context of blind separation of independent sources. We derive a simple form of the learning algorithm which allows us not only to adapt the separation matrix coefficients but also the shape of the nonlinear functions. A comparison with the mixture-of-densities approach is also presented on some experimental data that demonstrates the effectiveness and efficiency of the proposed method