Personalization of Hearing Aid Compression by Human-in-the-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 may not be 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-the-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. These results demonstrate the proof-of-concept of achieving personalized compression via human-in-the-loop deep reinforcement learning.