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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].
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
68T05 Learning and adaptive systems in artificial intelligence
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