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

被引：12

作者：

Alamdari, Nasim ^{[1
]}

Lobarinas, Edward ^{[2
]}

Kehtarnavaz, Nasser ^{[1
]}

机构：

[1] Univ Texas Dallas, Elect & Comp Engn Dept, Richardson, TX 75080 USA

[2] Univ Texas Dallas, Callier Ctr Commun Disorders, Richardson, TX 75080 USA

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Personalized audio compression; deep reinforcement learning; human-in-the-loop personalization; personalized hearing aid; hearing aid compression;

D O I：

10.1109/ACCESS.2020.3035728

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：203503 / 203515

页数：13

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