Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning

Existing prescriptive compression strategies used in hearing aid fitting are designed based on gain averages from a group of users which are not necessarily optimal for a specific user. Nearly half of hearing aid users prefer settings that differ from the commonly prescribed settings. This paper pre...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Alamdari, Nasim, Lobarinas, Edward, Kehtarnavaz, Nasser
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.07.2020
Subjects
Compression tests
Deep learning
Hearing aids
Optimization
Online AccessGet full text

Cover

More Information
Summary:Existing prescriptive compression strategies used in hearing aid fitting are designed based on gain averages from a group of users which are not necessarily optimal for a specific user. Nearly half of hearing aid users prefer settings that differ from the commonly prescribed settings. This paper presents a human-in-loop deep reinforcement learning approach that personalizes hearing aid compression to achieve improved hearing perception. The developed approach is designed to learn a specific user's hearing preferences in order to optimize compression based on the user's feedbacks. Both simulation and subject testing results are reported which demonstrate the effectiveness of the developed personalized compression.
ISSN:2331-8422