For the 2022 challenge, see below.
Data downloads
- The validation data for the 2024 edition of the challenge has now been released: https://plcchallenge2024pub.blob.core.windows.net/plcchallengearchive/plcchallenge24_val_v2.zip
- A tool to check and validate processing latency can be found here: https://plcchallenge2024pub.blob.core.windows.net/plcchallengearchive/latency_test_v2.zip
- The blind set has now been released, and can be found here: https://plcchallenge2024pub.blob.core.windows.net/plcchallengearchive/plc_challenge_2024_blind_release.zip
We recommend using the recently released new version of PLCMOS, which is part of the speechmos · PyPI(opens in new tab) package, to aid with development.
If you use this dataset in a publication please cite the following paper:
@inproceedings{diener2024icassp,
title = {The ICASSP 2024 Audio Deep Packet Loss Concealment Grand Challenge},
author = {Diener, Lorenz and Branets, Solomiya and Saabas, Ando and Cutler, Ross},
year = 2024,
month = apr,
booktitle = {{ICASSP} 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing},
code = {https://aka.ms/plc-challenge},
}The previous challenges are:
@inproceedings{diener2022interspeech,
title = {INTERSPEECH 2022 Audio Deep Packet Loss Concealment Challenge},
author = {Diener, Lorenz and Sootla, Sten and Branets, Solomiya and Saabas, Ando and Aichner, Robert and Cutler, Ross},
year = 2022,
month = sep,
booktitle = {{INTERSPEECH} 2022 - 22nd Annual Conference of the International Speech Communication Association},
doi = {10.21437/Interspeech.2022-10829},
code = {https://aka.ms/plc-challenge},
}This repository will contain data and example code for the INTERSPEECH 2022 Audio Deep Packet Loss Concealment Challenge.
You can find more information about the challenge and how to enter at https://aka.ms/plc_challenge
If you have any questions, please contact us via e-mail at plc_challenge@microsoft.com
The training and validation dataset has now been released and is available as a tar.gz archive:
http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/test_train.tar.gz
The blind set is now also available:
http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/blind.tar.gz
Update (24. March 2022): The reference data for the blind set is now available:
http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/blind_set_reference.tar.gz
Please make sure to submit your results by the deadline, March 8th 2022 23:59 AoE.
Additional information about the data included can be found in our challenge paper, and information about how to register for the challenge can be found at https://aka.ms/plc_challenge .
A multipart zip file download of the training set is available for people who cannot download it as one big file:
https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.001 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.002 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.003 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.004 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.005 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.006 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.007 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.008 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.009 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.010 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.011 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.012 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.013 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.014 https://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/split/test_train.zip.015
To help with model development, we will provide access to a prototype PLC-MOS neural model API which will provide MOS score estimates for audio files with packet loss concealment applied. For further details on how to get access to this API, refer to https://aka.ms/plc_challenge . You can find an API usage example in PLC-MOS-API-Example.ipynb .
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