Evaluating and Optimizing Hearing-Aid Self-Fitting Methods using Population Coverage

TL;DR

This paper introduces a new metric called population coverage to evaluate and optimize self-fitting hearing aid methods, enabling better configuration with fewer presets and without extensive user studies.

Contribution

It proposes a novel probabilistic model and algorithms for selecting presets that maximize population coverage in self-fitting hearing aids.

Findings

Presets selected by the proposed algorithms achieve higher coverage than clustering methods.

The approach effectively determines the number of increments for slider-based configurations.

Population coverage can be evaluated without costly user studies.

Abstract

Adults with mild-to-moderate hearing loss can use over-the-counter hearing aids to treat their hearing loss at a fraction of traditional hearing care costs. These products incorporate self-fitting methods that allow end-users to configure their hearing aids without the help of an audiologist. A self-fitting method helps users configure the gain-frequency responses that control the amplification for each frequency band of the incoming sound. This paper considers how to design effective self-fitting methods and whether we may evaluate certain aspects of their design without resorting to expensive user studies. Most existing fitting methods provide various user interfaces to allow users to select a configuration from a predetermined set of presets. We propose a novel metric for evaluating the performance of preset-based approaches by computing their population coverage. The population coverage estimates the fraction of users for which it is possible to find a configuration they prefer. A unique aspect of our approach is a probabilistic model that captures how a user's unique preferences differ from other users with similar hearing loss. Next, we develop methods for determining presets to maximize population coverage. Exploratory results demonstrate that the proposed algorithms can effectively select a small number of presets that provide higher population coverage than clustering-based approaches. Moreover, we may use our algorithms to configure the number of increments for slider-based methods.