Acoustic Echo Cancelation for High Noise Environments

Acoustic echo cancellation (AEC) is highly imperative for enhanced communication in noisy environments such as a car or a conference room. In this work, we present a dual-structured AEC architecture that improves both the convergence time and misadjustment of a conventional adaptive subband AEC algorithm in high noise environments. In this architecture, one part performs smooth adaptation while the other part performs fast adaptation; a convergence detector is implemented to facilitate switching between the fast and smooth adaptations. We propose the momentum normalized least mean square (MNLMS) algorithm for smooth adaptation and we implement the NLMS algorithm for fast adaptation. The current architecture provides up to 3-4 dB echo reduction improvement over a conventional adaptive subband AEC algorithm and it helps minimize near-end distortion and artifacts in the post-processed AEC output.