  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.
