Efficient Personalization of Amplification in Hearing Aids via Multi-band Bayesian Machine Learning

TL;DR

This paper introduces an efficient Bayesian machine learning method for personalizing hearing aid amplification, which is practical for field use and yields preferences significantly better than standard settings.

Contribution

It presents a novel, training-efficient personalization approach that treats frequency bands independently and uses Bayesian learning, improving speed and preference accuracy.

Findings

Achieves hearing preference estimation with fewer comparisons.

Provides personalized gain settings preferred six times more than standard.

Demonstrates practical, field-deployable personalization method.

Abstract

Personalization of the amplification function of hearing aids has been shown to be of benefit to hearing aid users in previous studies. Several machine learning-based personalization approaches have been introduced in the literature. This paper presents a machine learning personalization approach with the advantage of being efficient in its training based on paired comparisons which makes it practical and field deployable. The training efficiency of this approach is the result of treating frequency bands independent of one another and by simultaneously carrying out Bayesian machine learning in each band across all of the frequency bands. Simulation results indicate that this approach leads to an estimated hearing preference function close to the true hearing preference function in fewer number of paired comparisons relative to the previous machine learning approaches. In addition, a clinical experiment conducted on eight subjects with hearing impairment indicate that this training efficient personalization approach provides personalized gain settings which are on average six times more preferred over the standard prescriptive gain settings.