F-T-LSTM based Complex Network for Joint Acoustic Echo Cancellation and Speech Enhancement

Abstract

With the increasing demand for audio communication and online conference, ensuring the robustness of Acoustic Echo Cancellation (AEC) under the complicated acoustic scenario including noise, reverberation and nonlinear distortion has become a top issue. Although there have been some traditional methods that consider nonlinear distortion, they are still inefficient for echo suppression and the performance will be attenuated when noise is present. In this paper, we present a real-time AEC approach using complex neural network to better modeling the important phase information and frequency-time-LSTMs (F-T-LSTM), which scan both frequency and time axis, for better temporal modeling. Moreover, we utilize modified SI-SNR as cost function to make the model to have better echo cancellation and noise suppression (NS) performance. With only 1.4M parameters, the proposed approach outperforms the AECchallenge baseline by 0.27 in terms of Mean Opinion Score (MOS).

SCA: Streaming Cross-attention Alignment for Echo Cancellation

“…In recent years, deep neural network (DNN) based acoustic echo cancellation (AEC) methods have achieved a significant improvement over the traditional signal processing based methods. Deep complex convolution recurrent network (DC-CRN) designed for noise suppression [1] is modified for the AEC task with frequency-time LSTM (F-T-LSTM) network [2] to better learn the relationship between frequency bands for effectively suppressing echo. The main drawback of in-place DC-CRN is the larger number of parameters.…”