Room Impulse Response as a Prompt for Acoustic Echo Cancellation

TL;DR

This paper introduces a novel approach for acoustic echo cancellation that uses room impulse response prompts to enhance model generalization in unseen environments, demonstrating significant performance improvements over baseline methods.

Contribution

The study presents a new RIR-guided training method for AEC models and explores four RIR prompt fusion techniques to improve real-world echo cancellation performance.

Findings

Significant performance gains in unseen echo scenarios.

Effective RIR prompt fusion methods demonstrated.

Improved generalization in real environment tests.

Abstract

Data-driven acoustic echo cancellation (AEC) methods, predominantly trained on synthetic or constrained real-world datasets, encounter performance declines in unseen echo scenarios, especially in real environments where echo paths are not directly observable. Our proposed method counters this limitation by integrating room impulse response (RIR) as a pivotal training prompt, aiming to improve the generalization of AEC models in such unforeseen conditions. We also explore four RIR prompt fusion methods. Comprehensive evaluations, including both simulated RIR under unknown conditions and recorded RIR in real, demonstrate that the proposed approach significantly improves performance compared to baseline models. These results substantiate the effectiveness of our RIR-guided approach in strengthening the model's generalization capabilities.