A maximum likelihood approach to nonlinear convolutive blind source separation.

*(English)*Zbl 1178.94127
Rosca, Justinian (ed.) et al., Independent component analysis and blind signal separation. 6th international conference, ICA 2006, Charleston, SC, USA, March 5–8, 2006. Proceedings. Berlin: Springer (ISBN 3-540-32630-8/pbk). Lecture Notes in Computer Science 3889, 926-933 (2006).

Summary: A novel learning algorithm for blind source separation of post-nonlinear convolutive mixtures with non-stationary sources is proposed in this paper. The proposed mixture model characterizes both convolutive mixture and post-nonlinear distortions of the sources. A novel iterative technique based on Maximum Likelihood (ML) approach is developed where the Expectation-Maximization (EM) algorithm is generalized to estimate the parameters in the proposed model. The post-nonlinear distortion is estimated by using a set of polynomials. The sufficient statistics associated with the source signals are estimated in the E-step while in the M-step, the parameters are optimized by using these statistics. In general, the nonlinear maximization in the M-step is difficult to be formulated in a closed form. However, the use of polynomial as the nonlinearity estimator facilitates the M-step tractable and can be solved via linear equations.

For the entire collection see [Zbl 1096.94002].

For the entire collection see [Zbl 1096.94002].