Nonlinear Residual Echo Suppression Based on Gated Dual Signal Transformation LSTM Network

Abstract

Although adaptive filters play a vital role in the acoustic echo cancellation system, multiple factors prevent them from completely eliminating the echo signal. Consequently, additional suppression module is required and crucial for enhancing the echo cancellation performance. In this work, we propose a gated dual signal transformation LSTM network (Gated DTLN) that improves upon the recently developed Dual Signal Trans-formation LSTM Network for AEC (DTLN-aec). The gated convolution units are inserted to enhance filtering features in the time domain part of the model, while the echo reference signal is removed from the input of this part to reduce the complexity of the mask generator. The experimental results on different signal-to-echo ratio (SER) datasets demonstrate the superiority of our proposed method.