MISEP method for postnonlinear blind source separation

C.H. Zheng, D.S. Huang, Kang Li, George Irwin, Z.L. Sun

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)2557-2578
Number of pages22
JournalNeural Computation
Issue number9
Publication statusPublished - Sep 2007

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Neuroscience(all)

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