利用声音方向信息提升多麦克风回声消除效果
Multi-Channel Acoustic Echo Cancellation Based on Direction-of-Arrival Estimation
- 分两阶段：先用轻量网络预测声源方向，再融合方向与信号做回声消除
- 在多种声学环境下性能优于基线方法，方向估计精度高
- 适合需要高保真语音通信的场景，如会议系统、智能音箱
声学回声消除（AEC）是重要的语音信号处理技术，可去除麦克风信号中的回声，实现自然流畅的全双工语音通信。尽管单通道AEC应用广泛，多通道AEC可通过多个麦克风提供的空间线索获得更优性能。现有方法通常结合波束成形与深度神经网络（DNN）。本文提出一种两阶段算法，通过引入声源方向信息增强多通道AEC。首先训练一个轻量级DNN以预测声源方向；随后将预测的方向信息、多通道麦克风信号及单通道远端信号联合输入AEC网络，估计近端信号。实验结果表明，该方法性能优于基线模型，并在多样声学环境中表现出强泛化能力。
原文摘要 · Abstract (English)
Acoustic echo cancellation (AEC) is an important speech signal processing technology that can remove echoes from microphone signals to enable natural-sounding full-duplex speech communication. While single-channel AEC is widely adopted, multi-channel AEC can leverage spatial cues afforded by multiple microphones to achieve better performance. Existing multi-channel AEC approaches typically combine beamforming with deep neural networks (DNN). This work proposes a two-stage algorithm that enhances multi-channel AEC by incorporating sound source directional cues. Specifically, a lightweight DNN is first trained to predict the sound source directions, and then the predicted directional information, multi-channel microphone signals, and single-channel far-end signal are jointly fed into an AEC network to estimate the near-end signal. Evaluation results show that the proposed algorithm outperforms baseline approaches and exhibits robust generalization across diverse acoustic environments.
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