This Repostory contains the pretrained DTLN-aec model for real-time acoustic echo cancellation.

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Deep LearningDTLN-aec
Overview

DTLN-aec

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 is among the top-five models of the challenge. 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.

@article{westhausen2020acoustic,
  title={Acoustic echo cancellation with the dual-signal transformation LSTM network},
  author={Westhausen, Nils L. and Meyer, Bernd T.},
  journal={arXiv preprint arXiv:2010.14337},
  year={2020}
}

Author: Nils L. Westhausen (Communication Acoustics , Carl von Ossietzky University, Oldenburg, Germany)

This code is licensed under the terms of the MIT license.


Contents:

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.


Usage:

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.


This repository is still under construction.

Owner
Nils L. Westhausen
PhD candidate at the Communication Acoustics group at the University of Oldenburg. Working on speech enhancement and separation.
Nils L. Westhausen
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