A dataset for hearing aid personalization.
Dhruv Vyas, Ryan Brummet, Yumna Anwar, Justin Jensen, Erik Jorgen Jorgensen, Yu-Hsiang Wu, and Octav Chipara
Over-the-counter hearing aids enable more affordable and accessible hearing health care by shifting the burden of configuring the device from trained audiologists to end-users. A critical challenge is to provide users with an easy-to-use method for personalizing the many parameters which control sound amplification based on their preferences. This paper presents a novel approach to fitting hearing aids that provides a higher degree of personalization than existing methods by using user feedback more efficiently. Our approach divides the fitting problem into two parts. First, we discretize an initial 24-dimensional space of possible configurations into a small number of presets. Presets are constructed to ensure that they can meet the hearing needs of a large fraction of Americans with mild-to-moderate hearing loss. Then, an online agent learns the best preset by asking a sequence of pairwise comparisons. This learning problem is an instance of the multi-armed bandit problem. We performed a 35-user study to understand the factors that affect user preferences and evaluate the efficacy of multi-armed bandit algorithms. Most notably, we identified a new relationship between a user's preference and presets: a user's preference can be represented as one or more preference points in the initial configuration space with stronger preferences expressed for nearby presets (as measured by the Euclidean distance). Based on this observation, we have developed a Two-Phase Personalizing algorithm that significantly reduces the number of comparisons required to identify a user's preferred preset. Simulation results indicate that the proposed algorithm can find the best configuration with a median of 25 comparisons, reducing by half the comparisons required by the best baseline. These results indicate that it is feasible to configure over-the-counter hearing aids using a small number of pairwise comparisons without the help of professionals.
You can download the data from the study described in the paper below.
This folder contains five different files.
This file contains audiogram data of study participants.
Columns description :
- Subject Id : Id of participants recruited for the Study
- Right 250 - 8000 : Hearing loss in dB at 250, 500, 1000, 2000, 3000, 4000, 6000, 8000 Hz
- Left 250 - 8000 : Hearing loss in dB at 250, 500, 1000, 2000, 3000, 4000, 6000, 8000 Hz
Columns description :
- Subject Id : Id of participants recruited for the Study
- REAR frequencies 250 - 8000 : REAR at 65 dB SPL at 250, 500, 1000, 2000, 3000, 4000, 6000, 8000 Hz
- Test Ear : Ear used for study (Left or Right)
Columns description :
- Subject Ids : Id of participants recruited for the Study
- B_a_b : Probabilities of the participating preferring REAR a over b
REAR in a pairwise comparison.
Please refer to our paper for more details (Personalizing over-the-counter hearing aids using pairwise comparisons)
Columns description :
- Subject Ids : Id of participants recruited for the Study
- B_x : Borda scores of x REAR after exhaustive pairwise comparison. :
Columns description :
- Preset ID : Preset ID given to the REAR
- Freq 250 - 8000 : REAR at 65 dB SPL at 250, 500, 1000, 2000, 3000, 4000, 6000, 8000 Hz for given preset ID.
For questions please email Octav Chipara at [email protected]