• DocumentCode
    178902
  • Title

    Kernel-based identification of Hammerstein systems for nonlinear acoustic echo-cancellation

  • Author

    Van Vaerenbergh, Steven ; Azpicueta-Ruiz, Luis A.

  • Author_Institution
    Dept. of Commun. Eng., Univ. of Cantabria, Santander, Spain
  • fYear
    2014
  • fDate
    4-9 May 2014
  • Firstpage
    3739
  • Lastpage
    3743
  • Abstract
    Traditional acoustic echo cancelers use a linear model to represent the echo path. Nevertheless, many consumer devices include loudspeakers and audio power amplifiers that may generate significant nonlinear distortions, creating the need for acoustic echo cancelers to produce a nonlinear filter response. To address this issue, we propose a nonlinear acoustic echo cancellation algorithm based on the framework of kernel methods. We model the echo path as a Hammerstein system, and we propose a resource-efficient strategy to identify the nonlinear and linear parts. While the basic algorithm is presented as an iterative batch method, we show that a simple extension allows it to be used in online scenarios as well. Results for both types of scenarios show that the algorithm produces good results on a system with a clipping nonlinearity and a realistic room impulse response.
  • Keywords
    acoustic signal processing; echo suppression; iterative methods; nonlinear acoustics; nonlinear distortion; Hammerstein systems; acoustic echo cancelers; audio power amplifiers; iterative batch method; kernel methods; kernel-based identification; loudspeakers; nonlinear acoustic echo cancellation algorithm; nonlinear distortions; nonlinear filter response; realistic room impulse response; resource-efficient strategy; Echo cancellers; Kernel; Nonlinear acoustics; Nonlinear distortion; Signal processing algorithms; Speech; Hammerstein systems; acoustic echo cancellation; kernel methods; nonlinear distortions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
  • Conference_Location
    Florence
  • Type

    conf

  • DOI
    10.1109/ICASSP.2014.6854300
  • Filename
    6854300