A convolutive source separation method with self-optimizing non-linearities

This paper deals with the separation of two convolutively mixed signals. The proposed approach uses a recurrent structure adapted by a generic rule involving arbitrary separating functions. These functions should ideally be set so as to minimize the asymptotic error variance of the structure. However, these optimal functions are often unknown in practice. The proposed alternative is based on a self-adaptive (sub-)optimization of the separating functions, performed by estimating the projection of the optimal functions on a predefined set of elementary functions. The equilibrium and stability conditions of this rule and its asymptotic error variance are studied. Simulations are performed for real mixtures of speech signals. They show that the proposed approach yields much better performance than classical rules.