This Repostory contains the pretrained DTLN-aec model for real-time acoustic echo cancellation in TF-lite format. This model was handed in to the acoustic echo cancellation challenge (AEC-Challenge) organized by Microsoft. The DTLN-aec model reached the 3rd place. The results of the AEC-Challenge can be found here.
The model was trained on data from the DNS-Challenge and the AEC-Challenge reposetories.
The arXiv preprint can be found here. Please cite:
@INPROCEEDINGS{westhausen21_dtln_aec,
author={Westhausen, Nils L. and Meyer, Bernd T.},
booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={{Acoustic Echo Cancellation with the Dual-Signal Transformation LSTM Network}},
year={2021},
volume={},
number={},
pages={7138-7142},
doi={10.1109/ICASSP39728.2021.9413510}
}
Author: Nils L. Westhausen (Communication Acoustics , Carl von Ossietzky University, Oldenburg, Germany)
This code is licensed under the terms of the MIT license.
This repository contains three prtrained models of different size:
dtln_aec_128(model with 128 LSTM units per layer, 1.8M parameters)dtln_aec_256(model with 256 LSTM units per layer, 3.9M parameters)dtln_aec_512(model with 512 LSTM units per layer, 10.4M parameters)
The dtln_aec_512 was handed in to the challenge.
First install the depencies from requirements.txt
Afterwards the model can be tested with:
$ python run_aec.py -i /folder/with/input/files -o /target/folder/ -m ./pretrained_models/dtln_aec_512
Files for testing can be found in the AEC-Challenge respository. The convention for file names is *_mic.wav for the near-end microphone signals and *_lpb.wav for the far-end microphone or loopback signals. The folder audio_samples contains one audio sample for each condition. The *_processed.wav files are created by the dtln_aec_512 model.
