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 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.
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
The workshop will be in two parts. On the 16th there will be a half-day of tutorials. These will be designed for people new to working with hearing aid signal processing. The main workshop will then follow on the 17th.
For more information and to register, click here.