A Model-Based Hearing Compensation Method Using a Self-Supervised Framework
Yadong Niu, Nan Li, Xihong Wu, Jing Chen
Abstract
Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical hearing thresholds. This paper proposes a model-based hearing compensation method using a self-supervised framework with a given auditory model. The influence of outer/inner hair cells dysfunction was simulated in the auditory model. And then, a neural network was trained to compensate for the given hearing impairment. Both objective and subjective experiments were conducted to evaluate the present method, and the results showed that listeners are sensitive to the parameter controlling the contribution of outer hair cells dysfunction. Additionally, the result indicated that listeners significantly preferred the speech processed by the proposed method to the traditional perspective fitting.
BibTeX
@inproceedings{icassp2023_amodelbasedheari,
title = {A Model-Based Hearing Compensation Method Using a Self-Supervised Framework},
author = {Yadong Niu and Nan Li and Xihong Wu and Jing Chen},
booktitle = {ICASSP 2023},
year = {2023}
}