论文标题
通过人类界深的强化学习对助听器压缩的个性化
Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning
论文作者
论文摘要
助听器配件中使用的现有规定压缩策略是根据一组用户的增益平均值设计的，这些用户不一定对特定用户最佳。近一半的助听器用户更喜欢与普遍规定的设置不同的设置。本文提出了一种人类的深入增强学习方法，该方法个性化助听器压缩以提高听力感知。开发的方法旨在学习特定用户的听力偏好，以根据用户的反馈来优化压缩。据报道，模拟和受试者测试结果证明了开发的个性化压缩的有效性。
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.
