A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control

TL;DR

This paper introduces a deep learning framework using convolutional recurrent networks to improve multichannel acoustic feedback control, achieving better speech enhancement with lower computational costs in complex environments.

Contribution

It proposes a novel scalable deep learning approach combining spatial and temporal processing for acoustic feedback control, outperforming traditional methods.

Findings

Enhanced speech quality in noisy environments

Lower computational complexity compared to existing methods

Effective in real-world acoustic scenarios

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.