The ICASSP 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication and conferencing systems. Many recent AEC studies report reasonable performance on synthetic datasets where the train and test samples come from the same underlying distribution. However, the AEC performance often degrades significantly on real recordings. Also, most of the conventional objective metrics such as echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ) do not correlate well with subjective speech quality tests in the presence of background noise and reverberation found in realistic environments. In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 2,500 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source an online subjective test framework based on ITU-T P.808 for researchers to quickly test their results. The winners of this challenge will be selected based on the average P.808 Mean Opinion Score (MOS) achieved across all different single talk and double talk scenarios.

For more details about the challenge, please visit the challenge website and refer to the paper.

• The datasets directory contains the real and synthetic training datasets and real test sets.

1. Set up Git Large File Storage (LFS) for faster download of the datasets. First, download and install the Git LFS client. Then, set up Git LFS for your user account by running:

git lfs install

2. Clone the repository.

git clone https://github.com/microsoft/AEC-Challenge AEC-Challenge

If you use this dataset in a publication please cite the following paper:

@article{Sridar2020,
  title={ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets and Testing Framework},
  author={Kusha Sridhar, Ross Cutler, Ando Saabas, Tanel Parnamaa, Hannes Gamper, Sebastian Braun, Robert Aichner, Sriram Srinivasan},
  journal={arXiv preprint arXiv:2009.04972},
  year={2020},
  url={https://arxiv.org/pdf/2009.04972.pdf}
}

If you use the test framework in a publication please cite the following paper:

@article{naderi2020open,
  title={An Open source Implementation of ITU-T Recommendation P. 808 with Validation},
  author={Naderi, Babak and Cutler, Ross},
  journal={arXiv preprint arXiv:2005.08138},
  year={2020}
}

MICROSOFT PROVIDES THE DATASETS ON AN "AS IS" BASIS. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, GUARANTEES OR CONDITIONS WITH RESPECT TO YOUR USE OF THE DATASETS. TO THE EXTENT PERMITTED UNDER YOUR LOCAL LAW, MICROSOFT DISCLAIMS ALL LIABILITY FOR ANY DAMAGES OR LOSSES, INLCUDING DIRECT, CONSEQUENTIAL, SPECIAL, INDIRECT, INCIDENTAL OR PUNITIVE, RESULTING FROM YOUR USE OF THE DATASETS.

The datasets are provided under the original terms that Microsoft received such datasets. See below for more information about each dataset.

The datasets used in this project are licensed as follows:

1. Clean speech:

• https://librivox.org/; License: https://librivox.org/pages/public-domain/
• Edinburgh 56 speaker dataset: https://datashare.is.ed.ac.uk/handle/10283/2791; License: https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/license_text?sequence=11&isAllowed=y

2. Noise:

• Audioset: https://research.google.com/audioset/index.html; License: https://creativecommons.org/licenses/by/4.0/
• Freesound: https://freesound.org/ Only files with CC0 licenses were selected; License: https://creativecommons.org/publicdomain/zero/1.0/
• Demand: https://zenodo.org/record/1227121#.XRKKxYhKiUk; License: https://creativecommons.org/licenses/by-sa/3.0/deed.en_CA

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