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arXiv · 2505.15914

A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control

Yuan-Kuei Wu·Juan Azcarreta·Kashyap Patel·Buye Xu·Jung-Suk Lee·Sanha Lee·Ashutosh Pandey
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Abstract

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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Yuan-Kuei Wu, Juan Azcarreta, Kashyap Patel, Buye Xu, Jung-Suk Lee, Sanha Lee, Ashutosh Pandey. 2025-05-21. A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control. https://arxiv.org/abs/2505.15914

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