Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network

TL;DR

This paper proposes a fusion scheme combining adaptive filters and neural networks to improve acoustic echo cancellation by reducing both linear and non-linear residual echoes more effectively than existing methods.

Contribution

It introduces a novel combination of adaptive filtering and neural network techniques specifically designed for more efficient residual echo suppression in AEC.

Findings

The combined scheme significantly reduces residual echo compared to traditional methods.

Neural network suppression effectively handles non-linear residual echoes.

Experimental results validate the superiority of the proposed approach.

Abstract

Acoustic Echo Cancellation (AEC) plays a key role in voice interaction. Due to the explicit mathematical principle and intelligent nature to accommodate conditions, adaptive filters with different types of implementations are always used for AEC, giving considerable performance. However, there would be some kinds of residual echo in the results, including linear residue introduced by mismatching between estimation and the reality and non-linear residue mostly caused by non-linear components on the audio devices. The linear residue can be reduced with elaborate structure and methods, leaving the non-linear residue intractable for suppression. Though, some non-linear processing methods have already be raised, they are complicated and inefficient for suppression, and would bring damage to the speech audio. In this paper, a fusion scheme by combining adaptive filter and neural network is proposed for AEC. The echo could be reduced in a large scale by adaptive filtering, resulting in little residual echo. Though it is much smaller than speech audio, it could also be perceived by human ear and would make communication annoy. The neural network is elaborately designed and trained for suppressing such residual echo. Experiments compared with prevailing methods are conducted, validating the effectiveness and superiority of the proposed combination scheme.