Optimising Hearing Aid Fittings for Speech in Noise with a Differentiable Hearing Loss Model

TL;DR

This paper introduces a machine learning approach using a differentiable hearing loss model to optimize hearing aid fittings tailored for speech in noisy environments, outperforming standard prescriptive fittings.

Contribution

It presents a novel differentiable hearing loss model enabling data-driven customization of hearing aid fittings for various noisy settings.

Findings

Optimized fittings improve speech clarity in noise.

Custom fittings outperform standard prescriptive fittings.

Differentiable model enables effective back-propagation optimization.

Abstract

Current hearing aids normally provide amplification based on a general prescriptive fitting, and the benefits provided by the hearing aids vary among different listening environments despite the inclusion of noise suppression feature. Motivated by this fact, this paper proposes a data-driven machine learning technique to develop hearing aid fittings that are customised to speech in different noisy environments. A differentiable hearing loss model is proposed and used to optimise fittings with back-propagation. The customisation is reflected on the data of speech in different noise with also the consideration of noise suppression. The objective evaluation shows the advantages of optimised custom fittings over general prescriptive fittings.