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

TL;DR

This paper introduces a human-in-loop deep reinforcement learning method to personalize hearing aid compression, improving individual hearing perception by adapting to user preferences through feedback.

Contribution

It presents a novel reinforcement learning approach that personalizes hearing aid compression based on user feedback, surpassing traditional group-based strategies.

Findings

Simulation results show improved hearing perception.

Subject testing confirms personalized compression effectiveness.

Method outperforms standard prescriptive strategies.

Abstract

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.