Acoustic Echo Cancellation using Deep Complex Neural Network with Nonlinear Magnitude Compression and Phase Information
|Renhua Peng (CAS, China), Linjuan Cheng (CAS, China), Chengshi Zheng (CAS, China), Xiaodong Li (CAS, China)|
This paper describes a two-stage acoustic echo cancellation (AEC) and suppression framework for the INTERSPEECH2021 AEC Challenge. In the first stage, four parallel partitioned block frequency domain adaptive filters are used to cancel the linear echo components, where the far-end signal is delayed 0ms, 320ms, 640ms and 960ms for these four adaptive filters, respectively, thus a maximum 1280 ms time delay can be well handled in the blind test dataset. The error signal with minimum energy and its corresponding reference signal are chosen as the input for the second stage, where a gate complex convolutional recurrent neural network (GCCRN) is trained to further suppress the residual echo, late reverberation and environmental noise simultaneously. To improve the performance of GCCRN, we compress both the magnitude of the error signal and that of the far-end reference signal, and then the two compressed magnitudes are combined with the phase of the error signal to regenerate the complex spectra as the input features of GCCRN. Numerous experimental results show that the proposed framework is robust to the blind test dataset, and achieves a promising result with the P.808 evaluation.