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2021 ISCA Workshop on Machine Learning Challenges for Hearing Aids (Clarity-2021)
Online Event, 17th September, 2021
claritychallenge.github.io/clarity2021-workshop/=====================================================
IMPORTANT DATES
15th June, 2021 - Clarity Challenge and Workshop paper submission deadline
1st July, 2021 - Workshop registration opens
2nd August, 2021 - Paper acceptance notification to authors
17th September - Workshop / Clarity Challenge results announced
17th October, 2021 - Final Challenge paper submission (2 to 6 pages)
One of the biggest challenges for hearing-impaired listeners is understanding speech in the presence of background noise. Everyday social noise levels can have a devastating impact on speech intelligibility. Inability to communicate effectively can lead to social withdrawal and isolation. Disabling hearing impairment affects 360 million people worldwide, with that number increasing because of the ageing population. Unfortunately, current hearing aid technology is often ineffective in noisy situations. Although amplification can restore audibility, it does not compensate fully for the effects of hearing loss.
The aim of this one-day virtual workshop is to report on the Clarity Enhancement Challenge, the first-ever machine learning challenge targeted at helping those with a hearing impairment. The challenge was launched at the start of 2021, and is seeking to find new approaches to signal processing in hearing aids. (For details of the challenge please visit the Clarity Challenge website.)
The Clarity-2021 workshop will be focused on presenting the 1st Clarity Enhancement Challenge, but is also open to relevant non-challenge papers. Relevant research topics include (but are not limited to)
- Models of speech intelligibility and quality for normal and hearing impaired listeners
- Applications of auditory scene analysis
- Binaural technology for speech enhancement and source separation
- Multi-microphone processing technology
- Real-time approaches to speech enhancement
- Statistical model-driven approaches to hearing aid processing
- Audio quality & intelligibility assessment hearing aid and cochlear implant users
- Efficient and effective integration of psychoacoustic testing in machine learning
- Machine learning for diverse target listeners
- Machine learning models of hearing impairment
For further workshop information please visit the workshop website (
https://claritychallenge.github.io/clarity2021-workshop). For information on the Clarity Challenge visit the Clarity project page (
http://claritychallenge.org)
Organisers
The Clarity Project Team
Prof Michael Akeroyd, University of Nottingham
Prof Jon Barker, University of Sheffield
Prof Trevor Cox, University of Salford
Prof John Culling, University of Cardiff
Dr Simone Graetzer, University of Salford
Prof Graham Naylor, University of Nottingham
Eszter Porter, University of Nottingham
Dr Rhoddy Viveros Muñoz, University of Cardiff