The idea of this work is fine-tuning and personalizing compression ratios of hearing aid dynamic range compression. In order to decrease the number of user feedbacks and thus enable a practical implementation of the personalized compression, first a reward function is considered to model hearing preferences of a user in an asynchronous manner. This is achieved by carrying out A/B comparison between instances of two different compressed audios. Then, an agent is trained to maximize reward. Following figure shows a block diagram of this approach:

Figure 1: Block diagram of Human-in-the-Loop Deep Reinforcement Learning.

Below shows how data is passes through different blocks of the personalization framework both in training and testing modes. Please refer to [1] for more details.

Figure 2 Developed personalized compression DRL framework for (a) training mode and (b) operation mode.

A User's Guide is provided which describes how to run the codes.