Enhanced Residual Echo Cancellation using Estimation of Delay and Fast LMS/Newton Algorithm based on Autoregressive Model

Abstract

With the advent of packetized audio transmission, echo cancellation has put forth many challenging requirements. Using an adaptive filter echo cancellation algorithm only in the active region in which echo exists, its performance can be greatly increased, and its complexity can be reduced. The proposed scheme estimates the constant delay and locates the active regions and is integrated with the fast LMS/Newton algorithm for efficient realization of long adaptive filters. We assume that the input sequence to the adaptive filter can be modeled as an autoregressive (AR) process whose order may be kept much lower than the adaptive filter length. In addition, the proposed echo cancellation is further integrated with residual echo cancellation scheme based on the autoregressive (AR) analysis. Residual echo is whitened by the inverse filter using the estimated AR coefficients. Removing speech characteristics of the residual echo signal, the noise reduction system successfully reduces the power of residual echo as well as that of ambient noise. The result of this integration is a powerful echo cancellation scheme that has improved performance and higher suitability for VLSI designs providing efficient operation in packetized audio transmission, hands free telephony, and commercial mobile communications.