• DocumentCode
    1303136
  • Title

    Blind identification and separation of convolutively mixed independent sources

  • Author

    Wang, Jun ; He, Zhenya

  • Author_Institution
    Dept. of Radio Eng., Southeast Univ., Nanjing, China
  • Volume
    33
  • Issue
    3
  • fYear
    1997
  • fDate
    7/1/1997 12:00:00 AM
  • Firstpage
    997
  • Lastpage
    1002
  • Abstract
    A trispectra method for solving the m-input n-output (n≥m) wideband blind identification and signal separation problem with unknown number of sources m is presented. The method is universal in the sense that it does not impose any restriction on the probability distribution of the input signals provided that they are non-Gaussian. A criterion, which states a sufficient condition for identification and separation, has been proved. An algorithm is also developed based on the criterion, whose efficiency is verified by the simulations.
  • Keywords
    convolution; identification; probability; signal detection; spectral analysis; blind identification; convolutively mixed independent sources; m-input n-output problem; probability distribution; signal separation problem; trispectra method; Finite impulse response filter; MIMO; Maximum likelihood estimation; Probability distribution; Sensor phenomena and characterization; Signal processing algorithms; Source separation; Speech enhancement; Sufficient conditions; Wideband;
  • fLanguage
    English
  • Journal_Title
    Aerospace and Electronic Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9251
  • Type

    jour

  • DOI
    10.1109/7.599323
  • Filename
    599323