Source separation in post nonlinear mixtures: an entropy-based algorithm
Anas Abu Taleb, Christian Jutten, S. Olympieff · 2002
This paper proposes a new approach for sources separation in special nonlinear mixtures, called post nonlinear mixtures (PNL). We first explain the nice separability properties of these mixtures: solutions have almost the same indeterminacies as in instantaneous linear mixtures. The method proposed in this paper is based on the minimization of the mutual information, which needs the knowledge of source distributions or more exactly of log-derivative of source distributions (the so-called score functions). The algorithm consists of three adaptive blocks: one nonlinear block is devoted to adaptive estimation of source score functions, and drives the adaptation of the two other blocks estimating the linear and nonlinear parts of the mixtures. The paper finishes with experimental results which illustrate the efficiency of the algorithm.