用靠近扬声器的麦克风捕捉失真信号，提升语音回声消除效果。
Why Not Put a Microphone Near the Loudspeaker? A New Paradigm for Acoustic Echo Cancellation
- 在扬声器旁加一个麦克风，获取失真远端信号作为参考
- 通过维纳滤波抑制近端语音干扰，净化参考信号
- 结合深度网络处理残余回声和噪声，适合真实复杂场景
由于低成本扬声器和复杂房间声学导致的非线性失真，实际环境中声学回声消除（AEC）仍具挑战。本文提出双麦克风配置：在扬声器附近增设一个辅助参考麦克风，捕捉非线性失真的远端信号。尽管该信号被近端语音污染，我们设计基于维纳滤波的预处理模块，估计压缩时频掩码以抑制近端成分。经净化的参考信号可支持更有效的线性AEC阶段，其残差信号随后输入深度神经网络，联合实现回声与噪声抑制。实验表明，该方法在匹配测试集上优于基线模型；进一步在不匹配数据集上的测试显示，在强非线性条件下仍取得显著性能提升，验证了其在实际应用中对未知非线性失真的有效性。
原文摘要 · Abstract (English)
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.
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