MISEP Method for Postnonlinear Blind Source Separation

Authors:
Chun-Hou Zheng;De-Shuang Huang;Kang Li;George Irwin;Zhan-Li Sun
Affiliations:
Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China, and School of Information and Technology, Qufu Normal University, Rizhao, Sh ...;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China dshuang@iim.ac.cn;k.li@ee.qub.ac.uk;School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, Belfast BT7 1NN, U.K. g.irwin@ee.qub.ac.uk;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China sun_zhl@iim.ac.cn
Venue:
Neural Computation
Year:
2007

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Abstract

In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of three-layered perceptrons and a linear network are used as the unmixing system to separate sources in the postnonlinear mixtures, and another group of three-layered perceptron is used as the auxiliary network. The learning algorithm for the unmixing system is then obtained by maximizing the output entropy of the auxiliary network. The proposed method is applied to postnonlinear blind source separation of both simulation signals and real speech signals, and the experimental results demonstrate its effectiveness and efficiency in comparison with existing methods.