A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation

TL;DR

This paper introduces a deep complex multi-frame filtering network for stereophonic acoustic echo cancellation, improving echo suppression in hands-free communication systems with noisy environments.

Contribution

It proposes a novel two-stage deep learning framework with multi-frame filtering and spectral decoupling for enhanced echo cancellation performance.

Findings

Achieves state-of-the-art echo cancellation results

Effective in low SNR and noisy conditions

Outperforms previous algorithms in various scenarios

Abstract

In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, various types of noise in communication environments further reduce speech quality and intelligibility. It is difficult to extract the near-end signal from the microphone signal within one step, especially in low signal-to-noise ratio scenarios. In this paper, we propose a deep complex network approach to address this issue. Specially, we decompose the stereophonic acoustic echo cancellation into two stages, including linear stereophonic acoustic echo cancellation module and residual echo suppression module, where both modules are based on deep learning architectures. A multi-frame filtering strategy is introduced to benefit the estimation of linear echo by capturing more inter-frame information. Moreover, we decouple the complex spectral mapping into magnitude estimation and complex spectrum refinement. Experimental results demonstrate that our proposed approach achieves stage-of-the-art performance over previous advanced algorithms under various conditions.