TY - GEN
T1 - A maximum likelihood approach to nonlinear convolutive blind source separation
AU - Zhang, Jingyi
AU - Khor, Li Chin
AU - Woo, Wai Lok
AU - Dlay, Satnam Singh
PY - 2006/7/11
Y1 - 2006/7/11
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/33745711468
U2 - 10.1007/11679363_115
DO - 10.1007/11679363_115
M3 - Conference contribution
AN - SCOPUS:33745711468
SN - 9783540326304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 926
EP - 933
BT - Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings
PB - Springer
T2 - 6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006
Y2 - 5 March 2006 through 8 March 2006
ER -