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.

中文翻译：

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通过人工循环深度强化学习实现助听器压缩的个性化

用于助听器验配的现有规定压缩策略是基于一组用户的增益平均值设计的，这些平均值不一定对特定用户是最佳的。近一半的助听器用户更喜欢与通常规定的设置不同的设置。本文提出了一种人机循环深度强化学习方法，该方法可个性化助听器压缩，以改善听力感知。开发的方法旨在了解特定用户的听力偏好，以便根据用户的反馈优化压缩。报告了模拟和主题测试结果，证明了所开发的个性化压缩的有效性。