• DocumentCode
    418558
  • Title

    Blind separation with Gaussian mixture model for convolutively mixed sources

  • Author

    Ohata, Masashi ; Mukai, Toshiharu ; Matsuoka, Kiyotoshi

  • Author_Institution
    Biologically Integrative Sensors Lab., RIKEN BMC Res. Center, Nagoya, Japan
  • Volume
    5
  • fYear
    2004
  • fDate
    23-26 May 2004
  • Abstract
    This paper proposes an online blind separation algorithm with Gaussian mixture model for convolutively mixed sources. Although similar algorithms were proposed, they were derived for independent and identically distributed (iid) sources. They may not work for sources which are not made iid by any linear filter. From the theoretical viewpoint, our algorithm also works well for the sources and search for an optimal separator simultaneously, it can be applied to the case where their statistical properties are quite unknown, except that sources are nonGaussian.
  • Keywords
    Gaussian noise; blind source separation; convolution; Gaussian mixture model; convolutively mixed sources; identically distributed sources; iid sources; independent distributed sources; linear filters; nonGaussian source; online blind separation algorithm; optimal separator; statistical properties; Biological system modeling; Biology; Biosensors; Blind source separation; Brain modeling; Electronic mail; Neural networks; Nonlinear filters; Particle separators; Source separation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
  • Print_ISBN
    0-7803-8251-X
  • Type

    conf

  • DOI
    10.1109/ISCAS.2004.1329446
  • Filename
    1329446