A Deep Hybrid Model for Stereophonic Acoustic Echo Control

Abstract

This paper proposes a deep hybrid model for stereophonic acoustic echo cancellation (SAEC). A two-stage model is considered, i.e., a deep-learning-based Kalman filter (DeepKalman) and a gated convolutional recurrent network (GCRN)-based postfilter, which are jointly trained in an end-to-end manner. The difference between the proposed DeepKalman filter and the conventional one is twofold. First, the input signal of the DeepKalman filter is a combination of the original two far-end signals and the nonlinear reference signal estimated from the microphone signal directly. Second, a low-complexity recurrent neural network is utilized to estimate the covariance of the process noise, which can enhance the tracking capability of the DeepKalman filter. In the second stage, we adopt GCRN to suppress residual echo and noise by estimating complex masks applied to the output signal of the first stage. Computer simulations confirm the performance advantage of the proposed method over existing SAEC algorithms.