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¶
Workshops¶
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:
CEC1: