• DocumentCode
    337391
  • Title

    Minimum entropy algorithms for source separation

  • Author

    Wu, Hsiao-Chun ; Principe, Jose C.

  • Author_Institution
    Lab. of Comput. Neuro-Eng., Florida Univ., Gainesville, FL, USA
  • fYear
    1998
  • fDate
    9-12 Aug 1998
  • Firstpage
    242
  • Lastpage
    245
  • Abstract
    The minimum entropy or maximum likelihood estimation can be utilized in blind source separation problem. Based on the local generalized Gaussian probability density model, a set of general anti-Hebbian rules can be derived. This set of adaptation rules give promising results when we test the real recordings
  • Keywords
    Gaussian processes; Hebbian learning; maximum likelihood estimation; minimum entropy methods; signal detection; adaptation rules; blind source separation problem; general anti-Hebbian rules; local generalized Gaussian probability density model; maximum likelihood estimation; minimum entropy algorithms; Blind source separation; Entropy; Equations; Finite impulse response filter; Gaussian distribution; Maximum likelihood estimation; Noise reduction; Probability density function; Source separation; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1998. Proceedings. 1998 Midwest Symposium on
  • Conference_Location
    Notre Dame, IN
  • Print_ISBN
    0-8186-8914-5
  • Type

    conf

  • DOI
    10.1109/MWSCAS.1998.759478
  • Filename
    759478
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=337391