Preprint
Evaluating and Optimizing Hearing-Aid Self-Fitting Methods using Population Coverage
ArXiv.org
10/24/2022
DOI: 10.48550/arXiv.2210.13732
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.
Details
- Title: Subtitle
- Evaluating and Optimizing Hearing-Aid Self-Fitting Methods using Population Coverage
- Creators
- Dhruv VyasErik JorgensenYu-Hsiang WuOctav Chipara
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arXiv.2210.13732
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 10/24/2022
- Academic Unit
- Communication Sciences and Disorders; Computer Science
- Record Identifier
- 9984306759802771
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