We are organising a series of machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise (Clarity) and speech-in-music (Cadenza). For further details of the Clarity Project visit the Clarity project website, and for details of our latest Clarity challenges visit our challenge documentation site. You can contact the Clarity Team by email at [email protected]. For further details of the Cadenza Project visit the Cadenza project website, and to find out about the latest Cadenza challenges join the Cadenza Challenge Group.
In this repository, you will find code to support all Clarity and Cadenza Challenges, including baselines, toolkits, and systems from participants. We encourage you to make your system/model open source and contribute to this repository.
- The ICASSP 2024 Cadenza Challenge (CAD_ICASSP_2024) is now open. π₯π₯
- Join the Cadenza Challenge Group to keep up-to-date on developments.
- Visit the Cadenenza Challenge website for more details.
- The first Cadenza Challenge (CAD1) is closed.
- Subjective Evaluation is underway. π
- The 2nd Clarity Prediction Challenge (CPC2) is now open. π₯π₯
- Join the Clarity Challenge Group to keep up-to-date on developments.
- Visit the Clarity Challenge website for more details.
- Evaluation tools and a baseline system will be available shortly.
- The 4th Clarity Workshop will be held as a satellite event of Interspeech 2023. For details visit the workshop website.
Clarity is available on the Python Package Index (PyPI) to install create and/or
activate a virtual environment and then use pip
to install.
conda create --name clarity python=3.8
conda activate clarity
pip install pyclarity
# First clone the repo
git clone https://github.com/claritychallenge/clarity.git
cd clarity
# Second create & activate environment with conda, see https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html
conda create --name clarity python=3.8
conda activate clarity
# Last install with pip
pip install -e .
Alternatively pip
allows you to install packages from GitHub sources directly. The following will install the current
main
branch.
pip install -e git+https://github.com/claritychallenge/clarity.git@main
Current challenge
Previous challenges
- The 1st Cadenza Challenge (CAD1)
- The 2nd Clarity Prediction Challenge (CPC2)
- The ICASSP 2023 Enhancement Challenge
- The 2nd Clarity Enhancement Challenge (CEC2)
- The 1st Clarity Prediction Challenge (CPC1)
- The 1st Clarity Enhancement Challenge (CEC1)
We provide also a number of tools in this repository:
- Hearing loss simulation
- Cambridge MSBG hearing loss simulator: descriptions can be found in the CEC1
description; an usage example can be found in the CEC1 baseline
evaluation script
evaluate.py
.
- Cambridge MSBG hearing loss simulator: descriptions can be found in the CEC1
description; an usage example can be found in the CEC1 baseline
evaluation script
- Objective intelligibility measurement
- Modified binaural STOI (MBSTOI): a Python implementation of MBSTOI. It is jointly used with the MSBG hearing loss model in the CEC1 baseline. The official matlab implementation can be found here: http://ah-andersen.net/code/
- Hearing-aid speech perception index (HASPI): a Python implementation of HASPI Version 2, and the better-ear HASPI for binaural speech signals. For official matlab implementation, request here: https://www.colorado.edu/lab/hearlab/resources
- Hearing-aid speech quality index (HASQI): a Python implementation of HASQI Version 2, and the better-ear HASQI for binaural speech signals.
- Hearing-aid audio quality index (HAAQI): a Python implementation of HAAQI.
- Hearing aid enhancement
- Cambridge hearing aid fitting (CAMFIT): a Python implementation of CAMFIT, translated from the HΓΆrTech Open Master Hearing Aid (OpenMHA); the CAMFIT is used together with OpenMHA enhancement as the CEC1 baseline, see
enhance.py
. - NAL-R hearing aid fitting: a Python implementation of NAL-R prescription fitting. It is used as the CEC2 baseline, see
enhance.py
.
- Cambridge hearing aid fitting (CAMFIT): a Python implementation of CAMFIT, translated from the HΓΆrTech Open Master Hearing Aid (OpenMHA); the CAMFIT is used together with OpenMHA enhancement as the CEC1 baseline, see
In addition, differentiable approximation to some tools are provided:
- Differentiable MSBG hearing loss model. See also the BUT implementation: https://github.com/BUTSpeechFIT/torch_msbg_mbstoi
- Differentiable HASPI (coming)
- CPC1:
- CEC1: