We are pleased to announce the launch of the first Clarity Enhancement Challenge for Hearing Aid Signal Processing. Details are available at the Clarity Challenge website (
). Sample data is available now for download; the full dataset and tools will be released on 15th February 2021.
for the systems that top our evaluation.
We are organising a series of machine learning challenges to advance hearing aid speech signal processing. Even if you’ve not worked on hearing aids before, we’ll provide you with the tools to enable you to apply your machine learning and speech processing algorithms to help those with a hearing loss.
Although age-related hearing loss affects 40% of 55 to 74 year-olds, the majority of adults who would benefit from hearing aids don’t use them. A key reason is simply that hearing aids don’t provide enough benefit. In particular, speech in noise is still a critical problem, even for the most sophisticated devices. The purpose of the “Clarity” challenges is to catalyse new work to radically improve the speech intelligibility provided by hearing aids.
The series of challenges will consider increasingly complex listening scenarios. The first round focusses on speech in indoor environments in the presence of a single interferer. It begins with a challenge involving improving hearing aid processing. Future challenges on how to model speech-in noise perception will be launched at a later date.
The task
You will be provided with simulated scenes, each including a target speaker and interfering noise. For each scene, there will be signals that simulate those captured by a behind-the-ear hearing aid with 3-channels at each ear and those captured at the eardrum without a hearing aid present. The target speech will be a short sentence and the interfering noise will be either speech or domestic appliance noise.
The task will be to deliver a hearing aid signal processing algorithm that can improve the intelligibility of the target speaker for a specified
hearing-impaired listener. Initially, entries will be evaluated using an objective speech intelligibility measure. Subsequently, up to twenty of the most promising systems will be evaluated by a panel of listeners.
We will provide a baseline system so that teams can choose to focus on individual components or to develop their own complete pipelines.
What will be provided- Evaluation of the best entries by a panel of hearing-impaired listeners.
- Speech + interferer scenes for training and evaluation.
- An entirely new database of 10,000 spoken sentences
- Listener characterisations including audiograms and speech-in-noise testing.
- Software including tools for generating training data, a baseline hearing aid algorithm, a baseline model of hearing impairment, and a binaural objective intelligibility measure.
Challenge and workshop participants will be invited to contribute to a journal Special Issue on the topic of Machine Learning for Hearing Aid Processing that will be announced next year.
For further information
If you are interested in participating and wish to receive further information, please sign up to the Clarity Forum at
http://claritychallenge.org/sign-up-to-the-challenges If you have questions, contact us directly at
contact@xxxxxxxxxxxxxxxxxxxxOrganisers
Prof. Jon Barker, Department of Computer Science, University of Sheffield
Prof. Michael A. Akeroyd, Hearing Sciences, School of Medicine, University of Nottingham
Prof. Trevor J. Cox, Acoustics Research Centre, University of Salford
Prof. John F. Culling, School of Psychology, Cardiff University
Prof. Graham Naylor, Hearing Sciences, School of Medicine, University of Nottingham
Dr Simone Graetzer, Acoustics Research Centre, University of Salford
Dr Rhoddy Viveros Muñoz, School of Psychology, Cardiff University
Eszter Porter, Hearing Sciences, School of Medicine, University of Nottingham
Funded by the Engineering and Physical Sciences Research Council (EPSRC), UK
Supported by RNID (formerly Action on Hearing Loss), Hearing Industry Research Consortium, Amazon TTS Research, Honda Research Institute Europe
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Professor Jon Barker,
Department of Computer Science,
University of Sheffield
+44 (0) 114 222 1824