A Neural Network-Based Nonlinear Acoustic Echo Canceller

Abstract

In this letter, we introduce a novel approach for nonlinear acoustic echo cancellation. The proposed approach uses the principle of transfer learning to train a neural network that approximates the nonlinear function responsible for the nonlinear distortions and generalizes this network to different acoustic conditions. The topology of the proposed network is inspired by the conventional adaptive filtering approaches for nonlinear acoustic echo cancellation. The network is trained to model the nonlinear distortions using the conventional error backpropagation algorithm. In deployment, and in order to account for any variation or discrepancy between training and deployment conditions, only a subset of the network's parameters is adapted using the significance-aware elitist resampling particle filter. The proposed approach is evaluated and verified using synthesized nonlinear distortions and real nonlinear distortions recorded by a commercial mobile phone.