Introduction

pyClarity is core data used across all Clarity Challenges and provides baselines, toolkits and systems for working with the challenge data. It can be used for scene generation, hearing aid modelling and HASPI speech intelligibility model to evaluate solutions.

Tutorials

A series of Jupyter Notebooks are available on Colab to help you learn how to use the tools made available within this package. To access these please refer to the Clarity Tutorials.

Challenges

• 2nd Clarity Enhancement Challenge (CEC2) (GitHub Recipes)

• 1st Clarity Enhancement Challenge (CEC1) (GitHub Recipes)

• 1st Clarity Prediction Challenge (CPC1) (GitHub Recipes)

Workshops

• Clarity 2021

• Clarity 2022

Tools

A number of tools are included 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.

• 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 enhancement

  • Cambridge hearing aid fitting (CAMFIT): a python implementation of CAMFIT, translated from the HörTech 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.

In addition, differentiable approximation to some tools are provided:

• [x] Differentiable MSBG hearing loss model. See also the BUT implementation: https://github.com/BUTSpeechFIT/torch_msbg_mbstoi

• [ ] Differentiable HASPI (coming)

Open-source systems

• CPC1:

  • Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired Listeners

  • Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction

• CEC1:

  • A Two-Stage End-to-End System for Speech-in-Noise Hearing Aid Processing