Attention-Enhanced Short-Time Wiener Solution for Acoustic Echo Cancellation

TL;DR

This paper introduces an attention-enhanced short-time Wiener approach for acoustic echo cancellation, combining classical filter theory with attention mechanisms to improve performance and robustness against double-talk interference.

Contribution

It presents a novel integration of attention mechanisms with the short-time Wiener solution, bridging traditional filter theory and deep learning for improved AEC.

Findings

Outperforms baseline models in accuracy

Enhances robustness against double-talk interference

Demonstrates better generalization in experiments

Abstract

Acoustic Echo Cancellation (AEC) is an essential speech signal processing technology that removes echoes from microphone inputs to facilitate natural-sounding full-duplex communication. Currently, deep learning-based AEC methods primarily focus on refining model architectures, frequently neglecting the incorporation of knowledge from traditional filter theory. This paper presents an innovative approach to AEC by introducing an attention-enhanced short-time Wiener solution. Our method strategically harnesses attention mechanisms to mitigate the impact of double-talk interference, thereby optimizing the efficiency of knowledge utilization. The derivation of the short-term Wiener solution, which adapts classical Wiener solutions to finite input causality, integrates established insights from filter theory into this method. The experimental outcomes corroborate the effectiveness of our proposed approach, surpassing other baseline models in performance and generalization. The official code is available at https://github.com/ZhaoF-i/ASTWS-AEC