Extended Variational Message Passing for Automated Approximate Bayesian Inference

Sat, 26 Jun 2021 16:37:31 +0200

ForneyLab: A Toolbox for Biologically Plausible Free Energy Minimization in Dynamic Neural Models

Sun, 23 Sep 2018 13:42:00 +0200

Acoustic scene classification from few examples

Sun, 09 Sep 2018 09:07:00 +0200

K-shot learning of acoustic context

Fri, 08 Dec 2017 16:37:31 +0200

CoHear

Wed, 01 Feb 2017 00:00:00 +0000

Hearing loss is a very serious health condition that has been associated with early dementia and cognitive decline. Still, hearing aid market penetration is quite low, in particular for the large population that is afflicted with ‘mild-to-moderate’ hearing impairment. This is mainly due to two reasons: Stigma (association with old age) and hearing aids (HA) sound quality. The recent commercial introduction of fashionable ‘hearables’ will likely alleviate the stigma issue. Recent advances in machine learning and cloud computing open new avenues for attacking the sound quality issue for hearing aids. In this project, we intend to develop a (crowd-based) collaborative design approach to improving the sound quality issues for the mild-to-moderately hearing-impaired population.

We expect to build a working prototype for collaboratively designed hearing algorithms that can be applied to a new class of ‘smart hearing devices’ with high appeal to the mild-to-moderately hearing-impaired patient. As an additional benefit, we hope that our technology will ease the transition from hearables to professional hearing aid technology for the moderate-to-profound hearing-impaired population.

Ivan Bocharov | BIASlab

Extended Variational Message Passing for Automated Approximate Bayesian Inference

ForneyLab: A Toolbox for Biologically Plausible Free Energy Minimization in Dynamic Neural Models

Acoustic scene classification from few examples

K-shot learning of acoustic context

CoHear