Variational Bayesian Method for Temporally Correlated Source Separation

  • Qinghua Huang
  • Jie Yang
  • Yue Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


We propose to use a low order autoregressive (AR) model to describe the temporal structure of source. Then we adopt Variational Bayesian (VB) method for source separation which models additive noise into the mixing system. The approach integrates the source probabilistic model and noise probabilistic model. Its goal is to approximate the actual probability density function of hidden variables and parameters using its approximating posterior distribution by minimizing the misfit between them. The advantage of our VB-AR algorithm is that it can exploit the temporal nature of source signals and avoid overfitting in the separating process. This algorithm can also identify the AR order. Experiments on artifact and real-world speech signals are used to verify our proposed algorithms. As a result, the lower AR source model improves the separation. The performance of the algorithm is compared with that of i.i.d. separation algorithm and the second-order separation algorithm.


Independent Component Analysis Independent Component Analysis Source Separation Blind Source Separation Variational Bayesian 
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

  • Qinghua Huang
    • 1
  • Jie Yang
    • 1
  • Yue Zhou
    • 1
  1. 1.Institute of Image Processing & Pattern RecognitionShanghai Jiaotong UniversityShanghaiP.R. China

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