Machine Learning applied to problems in audiology
The Automatic LAnguage-independent Development of the Digits-In-Noise test (Aladdin)-project aims to create a fully automatic test development procedure for digit-in-noise hearing tests in various languages and for different target populations.
The rise of new digital tools for collecting data on scales never before seen in our field coupled with new modeling techniques from deep learning requires us to think about what computational infrastructure we need in order to fully enjoy the benefits and mitigate associated barriers.
Speech recognition software has become increasingly sophisticated and accurate due to progress in information technology. This project aims to examine the performance of speech recognition apps and to explore which audiological tests are a representative measure of the ability of these apps to convert speech into text.
Looking for questions Here’s an idea. Collect the problems in audiology that need AI solutions (Instead of solutions looking for a problem, we are looking for genuine problems looking for a solution.)…
Computational audiology, the augmentation of traditional hearing health care by digital methods, has potential to dramatically advance audiological precision and efficiency to address the global burden of hearing loss.