Acoustic echo cancellation (AEC) remains challenging in real-world environments due to nonlinear distortions caused by low-cost loudspeakers and complex room acoustics. To mitigate these issues, we introduce a dual-microphone configuration, where an auxiliary reference microphone is placed near the loudspeaker to capture the nonlinearly distorted far-end signal. Although this reference signal is contaminated by near-end speech, we propose a preprocessing module based on Wiener filtering to estimate a compressed time-frequency mask to suppress near-end components. This purified reference signal enables a more effective linear AEC stage, whose residual error signal is then fed to a deep neural network for joint residual echo and noise suppression. Evaluation results show that our method outperforms baseline approaches on matched test sets. To evaluate its robustness under strong nonlinearities, we further test it on a mismatched dataset and observe that it achieves substantial performance gains. These results demonstrate its effectiveness in practical scenarios where the nonlinear distortions are typically unknown.

翻译：声学回声消除（AEC）在现实环境中仍面临挑战，主要源于低成本扬声器引起的非线性失真及复杂的室内声学效应。为缓解这些问题，本文提出一种双麦克风配置方案，通过在扬声器附近放置辅助参考麦克风以捕获非线性失真的远端信号。尽管该参考信号受到近端语音的干扰，我们设计了一种基于维纳滤波的预处理模块，通过估计压缩时频掩码来抑制近端分量。经净化的参考信号可实现更高效的线性AEC处理阶段，其残余误差信号随后输入深度神经网络进行联合残余回声与噪声抑制。评估结果表明，本方法在匹配测试集上优于基线方案。为验证其在强非线性条件下的鲁棒性，我们在失配数据集上进一步测试，观察到该方法获得了显著的性能提升。这些结果证明了其在非线性失真通常未知的实际场景中的有效性。