Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning
This addresses the issue for hearing aid users who often prefer non-standard settings, offering a personalized solution.
The paper tackles the problem of suboptimal hearing aid compression due to one-size-fits-all prescriptions by developing a human-in-loop deep reinforcement learning approach to personalize compression based on individual user feedback, with simulation and subject testing demonstrating its effectiveness.
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.