@inproceedings{2828a60e8a954e03aecbdcc689e40fe0,
title = "A maximum likelihood approach to nonlinear convolutive blind source separation",
abstract = "A novel learning algorithm for blind source separation of postn-onlinear 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.",
author = "Jingyi Zhang and Khor, {Li Chin} and Woo, {Wai Lok} and Dlay, {Satnam Singh}",
year = "2006",
month = jul,
day = "11",
doi = "10.1007/11679363_115",
language = "English",
isbn = "9783540326304",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "926--933",
booktitle = "Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings",
address = "Germany",
note = "6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 ; Conference date: 05-03-2006 Through 08-03-2006",
}