A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control

2025-05-21

AI Summary

Traditional DSP-based acoustic feedback cancellation (AFC) methods suffer from slow convergence and poor robustness under strongly correlated feedback noise in multi-channel scenarios. To address this, we propose a lightweight deep learning framework integrating spatiotemporal modeling. Our key contributions are: (1) a novel Convolutional Recurrent Network (CRN) architecture that explicitly captures both temporal dependencies and spatial correlations inherent in feedback signals; and (2) an in-a-loop end-to-end training paradigm jointly optimizing teacher-forcing supervision and multi-channel Wiener filtering, enabling real-time, accurate closed-loop feedback modeling. Experiments demonstrate stable convergence under high reverberation and multi-source interference, with significant improvements in speech enhancement quality. The method reduces computational overhead by over 30%, while achieving high convergence speed, low latency, and strong robustness—making it suitable for resource-constrained real-time applications.

Technology Category

• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Speech

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Controls multichannel acoustic feedback in audio devices
• Enhances speech with lower computational demands
• Optimizes performance in complex acoustic environments

Innovation

• Convolutional Recurrent Network for spatial-temporal processing
• Three training methods optimizing performance
• Scalable framework for real-world applications