A Progressive Neural Network for Acoustic Echo Cancellation

Acoustic echo cancellation is a key issue in hand-free communication systems. In this paper, we proposed a hybrid signal processing and deep echo cancellation method, where a two-stage neural network is designed to remove residual echo progressively. For the personalized acoustic echo cancellation, we proposed to decouple the tasks of echo cancellation and target speech extraction, and introduced a speaker attentive module for personalized separation, where the ECAPA-TDNN is used for speaker embedding generation. The proposed method (ByteAudio-18) ranked first on both Track 1 and Track 2 in ICASSP 2023 AEC Challenge.