Blind-source separation based on decorrelation and nonstationarity

Fuliang Yin, Tiemin Mei, Jun Wang

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


In this paper, discrete-time blind-source separation (BSS) of instantaneous mixtures is studied. Decorrelation-based sufficient criteria for BSS of stationary and nonstionary sources are derived based on nonstationarity and nonwhiteness. A gradient algorithm is proposed based on these criteria. A batch-data algorithm and an on-line algorithm are developed based on the corollaries of the BSS criteria. These algorithms are especially useful for the separation of nonstationary sources. They are robust to additive white noises if the time-delayed decorrelation and the nonstationarity of the sources are considered simultaneously in the algorithms. Experiment results show the effectiveness and performance of the proposed algorithms.

Original languageEnglish
Pages (from-to)1150-1158
Number of pages9
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number5
Publication statusPublished - May 2007
Externally publishedYes


  • Blind-source separation (BSS)
  • Decorrelation
  • Natural gradient
  • Nonstationary processes
  • Second-order statistics (SOS)
  • Stationary processes


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