A comparative assessment of noise and non-linear echo effects in acoustic echo cancellation

This paper addresses the problem of adaptive filtering for acoustic echo cancellation in noisy and non-linear environments. The first contribution relates to a new analysis on the comparative impact of additive noise and non-linear echo on the performance of adaptive filtering for linear acoustic echo cancellation (AEC). A comprehensive performance assessment is reported, including echo return loss enhancement (ERLE), convergence time and system distance metrics. This work better highlights differences between algorithm performance than previously published work and sheds new light on algorithm behavior. Results show that, in non-linear and noisy environments, the normalized-least mean square (NLMS) algorithm gives similar performance to the more complex affine projection algorithm (APA). The more computationally efficient frequency block least mean square (FBLMS) algorithm is particularly adversly effected and gives poorer performance than the basic least mean square (LMS) approach. These observations question the persuit of increased computational efficiency and reduced convergence time over robustness to distortions. The second contribution relates to an original account of the effects of non-linear echo and noise which, perhaps surprisingly, are greater for the latter. This observation highlights the need for more comprehensive studies on the effects of non-linear distortion and supports continuing efforts to tackle non-linear echo.