用端到端神经网络实现无需传统方法的语音回声消除
E2E-AEC: Implementing an end-to-end neural network learning approach for acoustic echo cancellation
- 逐步学习+预训练模型初始化，提升回声抑制效果
- 注意力机制优化使参考信号与麦克风信号对齐更精准
- 结合语音活动检测，提升语音质量和回声清除能力
我们提出一种基于神经网络的端到端语音回声消除（E2E-AEC）方法，支持流式推理，不依赖传统线性回声消除（LAEC）技术和时延估计。该方法采用渐进式学习策略逐步增强回声抑制；通过使用预训练的基于LAEC的模型进行知识迁移，利用其训练经验；优化注意力机制，在注意力权重上施加损失函数，实现参考信号与麦克风信号的精确时间对齐；最后引入语音活动检测，在近端无语音时掩码网络输出，以提升语音质量并改善回声去除。实验在公开数据集上验证了方法的有效性。
原文摘要 · Abstract (English)
We propose a novel neural network-based end-to-end acoustic echo cancellation (E2E-AEC) method capable of streaming inference, which operates effectively without reliance on traditional linear AEC (LAEC) techniques and time delay estimation. Our approach includes several key strategies: First, we introduce and refine progressive learning to gradually enhance echo suppression. Second, our model employs knowledge transfer by initializing with a pre-trained LAECbased model, harnessing the insights gained from LAEC training. Third, we optimize the attention mechanism with a loss function applied on attention weights to achieve precise time alignment between the reference and microphone signals. Lastly, we incorporate voice activity detection to enhance speech quality and improve echo removal by masking the network output when near-end speech is absent. The effectiveness of our approach is validated through experiments conducted on public datasets.
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