Enhancement of Source Independence for Blind Source Separation

  • Kun Zhang
  • Lai-Wan Chan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3889)


When exploiting independent component analysis (ICA) to perform blind source separation (BSS), it is assumed that sources are mutually independent. However, in practice, the latent sources are usually dependent to some extent. Fortunately, if the sources are the same type of natural signals, they may be mutually independent in some frequency band, and dependent in other band. It is possible to make them mutually independent by temporal-filtering. In this paper we investigate ways to find the optimal filter for enhancing source independence in two scenarios. If none of the sources is known, we propose to adaptively estimate the filter and the de-mixing matrix simultaneously by minimizing the mutual information between outputs. Consequently the learned filter makes the filtered sources as independent as possible and the learned de-mixing matrix successfully separates the mixtures. If some source signals are available, we can estimate the filter more reliably by making the filtered sources as independent as possible. After that, with temporal-filtering as preprocessing, we can successfully perform BSS using ICA. Experiments on separating speech signals and images are presented.


Independent Component Analysis Innovation Process Independent Component Analysis Blind Source Separation Natural Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kun Zhang
    • 1
  • Lai-Wan Chan
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatin, Hong Kong

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