Wavelet transform domain LMS algorithm

  • Authors:

  • Srinath Hosur;Ahmed H. Tewfik

  • Affiliations:

  • Department of Electrical Engineering, University of Minnesota, Minneapolis, MN;Department of Electrical Engineering, University of Minnesota, Minneapolis, MN

  • Venue:
  • ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
  • Year:
  • 1993

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Abstract

This paper describes a new normalized wavelet domain least-mean-square (LMS) algorithm. It establishes the faster convergence rate of this algorithm as compared to time domain LMS. The wavelet domain LMS algorithm requires only real arithmetic. In its most basic form it has a computational complexity that is higher than that of the traditional LMS technique by a factor of cN where N is the length of the transformed vector (or sliding analysis window) and c is the length of the analysis wavelet. The paper discusses other pre-conditioning strategies that yield a faster convergence rate for a given fixed excess mean squared error. The paper also briefly describes low complexity implementations of the wavelet domain LMS algorithm. These implementations exploit the structure of the wavelet transform of the underlying stochastic process.