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Blind Source Separation Using Graphical Models

  • Te-Won Lee

Conclusions

We summarized our approaches for separating voices from mixed recordings. In the single channel case, a priori learned basis functions are used to model the temporal structure of the speech signals. A maximum likelihood approach is used to separate a voice from jazz music given only one mixed channel. In case of two microphones, the problem of separating two voices recorded by two microphones has been tackled. The mixing coefficients, time delays and reverberation coefficients are estimated using the maximum likelihood or infomax approach. The two approaches can be combined in a graphical model since both methods can be represented as data generative models where learning involves the representation of signals via the basis functions and inference involves the estimation of sources. The inference part in case of the single channel is nonlinear and linear in the two channel case.

Keywords

Independent Component Analysis Speech Signal Independent Component Analysis Source Separation Blind Source Separation 
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 Science + Business Media, Inc. 2005

Authors and Affiliations

  • Te-Won Lee
    • 1
  1. 1.Institute for Neural ComputationUniversity of CaliforniaSan Diego

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