A Probabilistic Modeling Approach to Hearing Loss Compensation

TL;DR

This paper introduces a probabilistic modeling framework for designing hearing aid algorithms that automatically infer optimal signal processing, fitting, and evaluation metrics, aiming to improve personalized hearing loss compensation.

Contribution

It presents a novel generative probabilistic model and message passing algorithms for automated hearing aid fitting and performance evaluation, enhancing personalization and efficiency.

Findings

The method provides a unified framework for fitting and evaluation.

Simulations demonstrate the approach's feasibility and potential for real-time implementation.

The probabilistic model improves personalization accuracy over traditional methods.

Abstract

Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the impairment of each specific patient. The lack of a fundamental HA fitting theory is a strong contributing factor to an unsatisfying sound experience for about 20% of hearing aid patients. This paper proposes a probabilistic modeling approach to the design of HA algorithms. The proposed method relies on a generative probabilistic model for the hearing loss problem and provides for automated inference of the corresponding (1) signal processing algorithm, (2) the fitting solution as well as a principled (3) performance evaluation metric. All three tasks are realized as message passing algorithms in a factor graph representation of the generative model, which in principle allows for fast implementation on hearing aid or mobile device hardware. The methods are theoretically worked out and simulated with a custom-built factor graph toolbox for a specific hearing loss model.