Acoustic echo and background noise can seriously degrade the intelligibility of speech. In practice, echo and noise suppression are usually treated as two separated tasks and can be removed with various digital signal processing (DSP) and deep learning techniques. In this paper, we propose a new cascaded model, magnitude and complex temporal convolutional neural network (MC-TCN), to jointly perform acoustic echo cancellation and noise suppression with the help of adaptive filters. The MC-TCN cascades two separation cores, which are used to extract robust magnitude spectra feature and to enhance magnitude and phase simultaneously. Experimental results reveal that the proposed method can achieve superior performance by removing both echo and noise in real-time. In terms of DECMOS, the subjective test shows our method achieves a mean score of 4.41 and outperforms the INTERSPEECH2021 AEC-Challenge baseline by 0.54.

中文翻译：

---

基于级联幅度和复杂掩模估计的联合回声消除和噪声抑制

回声和背景噪声会严重降低语音的清晰度。在实践中，回声和噪声抑制通常被视为两个独立的任务，可以通过各种数字信号处理 (DSP) 和深度学习技术来消除。在本文中，我们提出了一种新的级联模型，幅度和复杂时间卷积神经网络（MC-TCN），在自适应滤波器的帮助下联合执行声学回声消除和噪声抑制。MC-TCN 级联两个分离核心，用于提取稳健的幅度谱特征并同时增强幅度和相位。实验结果表明，所提出的方法可以通过实时去除回声和噪声来实现卓​​越的性能。在 DECMOS 方面，主观测试表明我们的方法平均得分为 4。