Package: pyclarity
Machine learning challenges for hearing aid processingWe 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 visitthe Clarity project website, and for details of our latest Clarity challenges visit ourchallenge documentation site. You can contact the Clarity Team by email atclaritychallengecontact@gmail.com. For further details of the Cadenza Project visitthe Cadenza project website, and to find out about the latest Cadenza challenges join theCadenza 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.Current EventsThe ICASSP 2024 Cadenza Challenge (CAD_ICASSP_2024) is now open. :fire::fire:Join theCadenza Challenge Groupto keep up-to-date on developments.Visit the Cadenenza Challengewebsitefor more details.The first Cadenza Challenge (CAD1) is closed.Subjective Evaluation is underway. :new:The 2nd Clarity Prediction Challenge (CPC2) is now open.   :fire::fire:Join theClarity Challenge Groupto keep up-to-date on developments.Visit the Clarity Challengewebsitefor 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 theworkshop website.InstallationPyPIClarity is available on thePython Package Index (PyPI)to install create and/or
activate a virtual environment and then usepipto install.condacreate--nameclaritypython=3.8
condaactivateclarity

pipinstallpyclarityGitHub Cloning# First clone the repogitclonehttps://github.com/claritychallenge/clarity.gitcdclarity# Second create & activate environment with conda, see https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.htmlcondacreate--nameclaritypython=3.8
condaactivateclarity# Last install with pippipinstall-e.GitHub pip installAlternativelypipallows you to install packages from GitHub sources directly. The following will install the currentmainbranch.pipinstall-egit+https://github.com/claritychallenge/clarity.git@mainChallengesCurrent challengeThe ICASSP 2024 Cadenza ChallengePrevious challengesThe 1st Cadenza Challenge (CAD1)The 2nd Clarity Prediction Challenge (CPC2)The ICASSP 2023 Enhancement ChallengeThe 2nd Clarity Enhancement Challenge (CEC2)The 1st Clarity Prediction Challenge (CPC1)The 1st Clarity Enhancement Challenge (CEC1)Available toolsWe provide also a number of tools in this repository:Hearing loss simulationCambridge MSBG hearing loss simulator: descriptions can be found in theCEC1
description; an usage example can be found in theCEC1 baselineevaluation scriptevaluate.py.Objective intelligibility measurementModified binaural STOI (MBSTOI): a Python implementation of MBSTOI. It is
jointly used with the MSBG hearing loss model in theCEC1 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/resourcesHearing-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 enhancementCambridge hearing aid fitting (CAMFIT): a Python implementation of CAMFIT, translated from theHörTech Open Master Hearing Aid (OpenMHA); the CAMFIT is used together with OpenMHA enhancement as theCEC1 baseline, seeenhance.py.NAL-R hearing aid fitting: a Python implementation of NAL-R prescription fitting. It is used as theCEC2 baseline, seeenhance.py.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_mbstoiDifferentiable HASPI (coming)Open-source systemsCPC1:Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in
Hearing-impaired ListenersUnsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility
PredictionCEC1:A Two-Stage End-to-End System for Speech-in-Noise Hearing Aid Processing