# Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2007.00192 · HTML: https://mlsanity.com/paper/2007.00192/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Pragmatic Image Compression for Human-in-the-Loop Decision-Making](https://mlsanity.com/paper/2108.04219/) (2021)
- [Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review](https://mlsanity.com/paper/2411.10268/) (2024)
- [Denoising Induction Motor Sounds Using an Autoencoder](https://mlsanity.com/paper/2208.04462/) (2022)
- [Music Generation using Human-In-The-Loop Reinforcement Learning](https://mlsanity.com/paper/2501.15304/) (2025)
- [AIDA: An Active Inference-based Design Agent for Audio Processing Algorithms](https://mlsanity.com/paper/2112.13366/) (2021)

## Abstract (arXiv)

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.