Dynamic Neural Network-based Solutions for Acoustic Echo Suppression

Abstract

Adaptive filters are widely used as core algorithms in current acoustic echo cancellation (AEC) systems. Echo path estimation is carried out by using different adaptive strategies in the adaptive filter. When nonlinear echo occurs, the performance deteriorates and seriously affects the call quality of both terminals. Taking advantage of the excellent non-linear fitting ability of neural networks in this paper, dynamic neural networks with self-updating parameters work continuously during the inference stage. The algorithm was computed and evaluated using publicly available echo audio data, showing that the dynamic neural network performs approximately as well as the optimal algorithm in the linear echo environment, and outperforms existing algorithms in the non-linear echo environment.