# Acoustic echo cancellation with the dual-signal transformation LSTM network

arXiv: https://arxiv.org/abs/2010.14337 · HTML: https://mlsanity.com/paper/2010.14337/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Deep model with built-in cross-attention alignment for acoustic echo cancellation](https://mlsanity.com/paper/2208.11308/) (2022)
- [Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression](https://mlsanity.com/paper/2308.11053/) (2023)
- [Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning](https://mlsanity.com/paper/2210.16791/) (2022)
- [Time-Variance Aware Real-Time Speech Enhancement](https://mlsanity.com/paper/2302.13063/) (2023)

## Abstract (arXiv)

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).