• DocumentCode
    2207087
  • Title

    Source separation of baseband signals in Post-Nonlinear mixtures

  • Author

    Duarte, L.T. ; Jutten, C. ; Rivet, B. ; Suyama, R. ; Attux, R. ; Romano, J. M T

  • Author_Institution
    GIPSA-Lab., Inst. Polytech. de Grenoble, St. Martin d´´Heres, France
  • fYear
    2009
  • fDate
    1-4 Sept. 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Usually, source separation in Post-Nonlinear (PNL) models is achieved via one-stage methods, i.e. the two parts (linear and nonlinear) of a PNL model are dealt with at the same time. However, recent works have shown that the development of two-stage techniques may simplify the problem. Indeed, if the nonlinear stage can be compensated separately, then, in a second moment, one can make use of the well-established source separation algorithms for the linear case. Motivated by that, we propose in this work a novel two-stage PNL method relying on the assumption that the sources are bandlimited signals. In the development of our method, special care is taken in order to make it as robust as possible to noise. Simulation results attest the effectiveness of the proposal.
  • Keywords
    source separation; baseband signal source separation algorithm; post-nonlinear mixture model; Acoustic noise; Baseband; Data mining; Entropy; Gaussian processes; Independent component analysis; Mutual information; Noise robustness; Signal processing; Source separation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on
  • Conference_Location
    Grenoble
  • Print_ISBN
    978-1-4244-4947-7
  • Electronic_ISBN
    978-1-4244-4948-4
  • Type

    conf

  • DOI
    10.1109/MLSP.2009.5306214
  • Filename
    5306214