• DocumentCode
    411334
  • Title

    Performance analysis of the adjusted step size NLMS algorithm

  • Author

    Kim, Joonwan ; Poularikas, Alexander D.

  • Author_Institution
    Sch. of Eng. & Eng. Technol., LeTourneau Univ., Longview, TX, USA
  • fYear
    2004
  • fDate
    2004
  • Firstpage
    467
  • Lastpage
    471
  • Abstract
    An adaptive noise canceller is a well-known method for removing noise from noise-corrupted speech. The problem arises in many situations such as airplane cockpits and automobiles. Tire adjusted step size NLMS (normalized least mean squares) algorithm incorporating a variable step size parameter whose values are based on the ratio of signal-to-noise power has very good convergence speed and low steady-state misadjustment. This paper extends the results of the adjusted step size NLMS algorithm [J. Kim et al., 2003] by investigating the adjusted step size NLMS algorithm approaches when large and abrupt changes of the desired signal as well as the noise signal are present. Simulation results are presented to compare the performance of the adjusted step size NLMS algorithm with the fixed step size LMS algorithm and other commonly used variable step size LMS algorithms.
  • Keywords
    adaptive filters; convergence of numerical methods; digital filters; interference suppression; least mean squares methods; speech processing; adaptive noise canceller; adjusted step size NLMS; airplane cockpits; automobiles; convergence speed; noise-corrupted speech; normalized least mean squares; signal-to-noise power; steady-state misadjustment; Adaptive filters; Adaptive signal processing; Automotive engineering; Filtering algorithms; Least squares approximation; Noise cancellation; Performance analysis; Signal processing algorithms; Signal to noise ratio; Size control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Theory, 2004. Proceedings of the Thirty-Sixth Southeastern Symposium on
  • ISSN
    0094-2898
  • Print_ISBN
    0-7803-8281-1
  • Type

    conf

  • DOI
    10.1109/SSST.2004.1295701
  • Filename
    1295701