用深度学习提升多通道音频反馈抑制效率，降低计算开销。
A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control
- 设计卷积循环网络融合空间与时间特征处理
- 三种训练策略优化复杂环境下的反馈抑制性能
- 适合需要低延迟、高鲁棒性的智能音频设备开发
本研究提出一种用于音频设备中多通道声反馈控制的深度学习框架。传统数字信号处理方法在处理高度相关噪声（如声反馈）时收敛困难。本文引入卷积循环网络，高效结合空间与时间特征处理，在显著提升语音增强能力的同时降低计算需求。采用三种训练方式：环内训练、教师强制训练及结合多通道维纳滤波的混合策略，有效优化复杂声学环境中的性能表现。该可扩展框架为实际应用提供稳健解决方案，推动声反馈控制技术的重要进展。
原文摘要 · Abstract (English)
This study presents a deep-learning framework for controlling multichannel acoustic feedback in audio devices. Traditional digital signal processing methods struggle with convergence when dealing with highly correlated noise such as feedback. We introduce a Convolutional Recurrent Network that efficiently combines spatial and temporal processing, significantly enhancing speech enhancement capabilities with lower computational demands. Our approach utilizes three training methods: In-a-Loop Training, Teacher Forcing, and a Hybrid strategy with a Multichannel Wiener Filter, optimizing performance in complex acoustic environments. This scalable framework offers a robust solution for real-world applications, making significant advances in Acoustic Feedback Control technology.
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