cpc1

The 1st Clarity Prediction Challenge (CPC1)

Clarity challenge code for the 1st prediction challenge (CPC1).

There are two traks in CPC1:

• closed-set: the evaluation systems and listeners are covered in the training set, the signals and listener repsonses are provieded in CPC1.train.json
• open-set: the evaluation systems and listeners are not seen in the training set, the signals and listener responses are provieded in CPC1_indep.train.json

For more information about the CPC1 please visit claritychallenge.org/docs/cpc1/cpc1_intro.

Data structure

To download the CPC1 data, please visit here.

clarity_CPC1_data.v1_1 contains the training data:

clarity_data
|
└───HA_outputs
|   |
|   └───train 3.8G
|   |
|   └───train_indep 2.8G
|
└───scenes 12.1G

metadata
    |CPC1.train.json
    |CPC1.train_indep.json
    |listener_data.CPC1_train.xlsx
    listeners.CPC1_train.json
    |scenes.CPC1_train.json

clarity_CPC1_data.test.v1 follows the same structure as clarity_CPC1_data.v1_1, except that the listener responses (i.e. test labels) are not included. The test listener responses are in the test_listener_responses.

Baseline

The baseline folder provides the code of the Cambridge Auditory Group MSBG hearing loss model and MBSTOI, see CEC1. Run run.py to generate the predicted intelligibility, and then run compute_scores.py to apply logistic fitting and compute the evaluation scores, including RMSE, normalised cross-correlation, Kendall's Tau coefficient.

Citing CPC1

@inproceedings{barker2022the,
  title={The 1st Clarity Prediction Challenge: A machine learning challenge for hearing aid intelligibility prediction},
  author={Jon Barker, Michael Akeroyd, Trevor J. Cox, John F. Culling, Jennifer Firth, Simone Graetzer, Holly Griffiths, Lara Harris, Graham Naylor, Zuzanna Podwinska, Eszter Porter and Rhoddy Viveros Munoz},
  year={2022}
}