用注意力机制改进传统维纳滤波，提升语音回声消除效果
Attention-Enhanced Short-Time Wiener Solution for Acoustic Echo Cancellation
- 结合注意力机制与短时维纳滤波，增强对双讲干扰的鲁棒性
- 在多个数据集上优于现有基线模型，泛化能力更强
- 适合需要高精度回声消除的实时通信系统开发者
声学回声消除（AEC）是实现自然全双工通信的关键语音信号处理技术，旨在消除麦克风输入中的回声。当前基于深度学习的AEC方法多聚焦于模型结构优化，常忽略传统滤波理论的知识融合。本文提出一种注意力增强的短时维纳解法（ASTWS-AEC），通过注意力机制有效抑制双讲干扰，提升知识利用效率。该方法将经典维纳解扩展至有限因果输入场景，融合了成熟的滤波理论洞见。实验结果表明，所提方法在性能和泛化性上均超越多个基线模型。官方代码已开源：https://github.com/ZhaoF-i/ASTWS-AEC。
原文摘要 · Abstract (English)
Acoustic Echo Cancellation (AEC) is an essential speech signal processing technology that removes echoes from microphone inputs to facilitate natural-sounding full-duplex communication. Currently, deep learning-based AEC methods primarily focus on refining model architectures, frequently neglecting the incorporation of knowledge from traditional filter theory. This paper presents an innovative approach to AEC by introducing an attention-enhanced short-time Wiener solution. Our method strategically harnesses attention mechanisms to mitigate the impact of double-talk interference, thereby optimizing the efficiency of knowledge utilization. The derivation of the short-term Wiener solution, which adapts classical Wiener solutions to finite input causality, integrates established insights from filter theory into this method. The experimental outcomes corroborate the effectiveness of our proposed approach, surpassing other baseline models in performance and generalization. The official code is available at https://github.com/ZhaoF-i/ASTWS-AEC
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