Acoustic echo cancellation with the dual-signal transformation LSTM network

TL;DR

This paper introduces the DTLN, a dual-signal transformation LSTM network, for real-time acoustic echo cancellation, achieving state-of-the-art results by combining time-frequency and time-domain features, including phase information.

Contribution

The paper presents a novel DTLN model that integrates dual-signal transformation with LSTM for improved acoustic echo cancellation in real-time scenarios.

Findings

Achieves state-of-the-art echo cancellation performance.

Outperforms baseline by 0.30 MOS score.

Robust to noisy and reverberant conditions.

Abstract

This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-time acoustic echo cancellation (AEC). The DTLN combines a short-time Fourier transformation and a learned feature representation in a stacked network approach, which enables robust information processing in the time-frequency and in the time domain, which also includes phase information. The model is only trained on 60~h of real and synthetic echo scenarios. The training setup includes multi-lingual speech, data augmentation, additional noise and reverberation to create a model that should generalize well to a large variety of real-world conditions. The DTLN approach produces state-of-the-art performance on clean and noisy echo conditions reducing acoustic echo and additional noise robustly. The method outperforms the AEC-Challenge baseline by 0.30 in terms of Mean Opinion Score (MOS).