Title :
Mutual Information Minimization for Under-Determined Blind Source Separation
Author :
Wang, Fuxiang ; Zhang, Jun
Author_Institution :
Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing, China
Abstract :
An important step of sparse representation technique for under-determined BSS (blind source separation) is the estimation of the mixing matrix. In this paper, a new method to estimate the mixing matrix is proposed. The objective is to find the mixing matrix to minimize the mutual information of the estimated sources. An algorithm for the learning of the mixing matrix is proposed by the natural gradient. Simulation results of speech separation demonstrate the effectiveness o f our method.
Keywords :
blind source separation; learning (artificial intelligence); matrix algebra; signal representation; speech processing; learning algorithm; mixing matrix estimation; mutual information minimization; natural gradient method; sparse representation technique; speech separation; under-determined blind source separation; Blind source separation; Clustering algorithms; Entropy; Laplace equations; Mutual information; Probability density function; Source separation; Sparse matrices; Speech;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5365757
