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.
journal_name
Neural Computjournal_title
Neural computationauthors
Zheng CH,Huang DS,Li K,Irwin G,Sun ZLdoi
10.1162/neco.2007.19.9.2557subject
Has Abstractpub_date
2007-09-01 00:00:00pages
2557-78issue
9eissn
0899-7667issn
1530-888Xjournal_volume
19pub_type
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