The acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and the far-end signal. Usually, a post-processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of dual-path recurrent neural network (DPRNN) to improve the quality of speech communication. Both the residual signal and the auxiliary signal, the far-end signal or the output of the adaptive filter, obtained from the linear acoustic echo cancelation are adopted to form a dual-stream for the DPRNN. We validate the efficacy of the proposed method in the notoriously difficult double-talk situations and discuss the impact of different auxiliary signals on performance. We also compare the performance of the time domain and the time-frequency domain processing. Furthermore, we propose an efficient and applicable way to deploy our method to off-the-shelf loudspeakers by fine-tuning the pre-trained model with little recorded-echo data.

中文翻译：

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基于双流 DPRNN 的非线性残余回声抑制

由于回声与远端信号之间的非线性关系，线性自适应滤波器不能完全去除声学回声。通常，需要一个后处理模块来进一步抑制回声。在本文中，我们提出了一种基于双路径递归神经网络（DPRNN）修改的残余回声抑制方法，以提高语音通信质量。采用线性声学回声消除得到的残余信号和辅助信号、远端信号或自适应滤波器的输出，形成DPRNN的双流。我们验证了所提出方法在众所周知困难的双方通话情况下的有效性，并讨论了不同辅助信号对性能的影响。我们还比较了时域和时频域处理的性能。此外，我们提出了一种有效且适用的方法，通过微调具有很少记录回声数据的预训练模型，将我们的方法部署到现成的扬声器。