On computationally-efficient NLMS-based algorithms for echo cancellation applications

Abstract

This paper investigates computationally-efficient adaptive algorithms suitable for echo cancellation applications. Power-of-two (PO2) quantization for the error and/or regressor vector is added to a selective-partialcoefficient-update algorithm (SPCUA) to further reduce the amount of computations. A simple stop-and-go procedure is included to save more computations when the generated error magnitude is below a certain limit. Simulation results with comparisons are included to show the advantages of the improved algorithms.

Computationally-Efficient DNLMS-Based Adaptive Algorithms for Echo Cancellation Application

“…The idea behind this algorithm is to "stop" adaptation or let it "go" based on the level of the error at the particular sampling time under consideration. In [1], the SAG concept is applied to NLMS in order to further reduce the amount of computations. In this SAG algorithm, when the magnitude of the error is below a pre-defined threshold, coefficient adaptation is stopped for that iteration.…”

“…In (8), κ is a positive real number and f (n) is the flag indicating whether or not to update the coefficients. In [1], κ was determined by observing the statistics of | e(n) | over a large number of iterations. Here, the SAG-threshold is related to the regressor energy.…”

“…One of the most common adaptive filtering algorithms used in echo cancellation is the NLMS algorithm. Recently, computationally-efficient techniques have been applied to NLMS for echo cancellation [1]. The modifications to NLMS included adding power-oftwo (POT) quantization [2] of the error and regressor, selective-partial coefficient update (namely the M-Max algorithm [3]), and a simple stop-and-go (SAG) algorithm.…”