Bayesian Active Learning in Audiology

Computational Audiology Network (CAN)

Here we discuss with Josef Schlittenlacher (ManCAD), Bert de Vries (TUe) and Dennis Barbour (WashU st. Louis) the potential of Bayesian active learning in audiology, in medicine, and beyond.
Quotes from the interview:
Dennis: 'No Bayesianists are born, they are all converted' (origin unknown)
Josef: The audiogram is the ideal testbed for Bayesian active learning.'
Bert's favorite quote: “Everything is the way it is because it got that way” (D'Arcy Wentworth Thompson, 1860--1948)
The later quote reflects on the idea that everything evolved to where it is now. It’s not a quote from the Free Energy Principle but it has everything to do with it. The hearing system evolved to where it is now. To design proper hearing aid algorithms, we should not focus on the best algorithm but rather on an adaptation process that converges to better algorithms than before.
Further reading and exploring:
- https://computationalaudiology.com/bayesian-active-learning-in-audiology/
- https://computationalaudiology.com/for-professionals/
- Audiogram estimation using Bayesian active learning, https://doi.org/10.1121/1.5047436
- Online Machine Learning Audiometry, https://pubmed.ncbi.nlm.nih.gov/30358656/
- Bayesian Pure-Tone Audiometry Through Active Learning Under Informed Priors, https://www.frontiersin.org/articles/10.3389/fdgth.2021.723348/full
- Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review, https://www.jmir.org/2022/2/e32581