This paper describes a neural network designed to provide aid in the preventive diagnosis of hearing loss issues. Hearing loss is a widely widespread disability affecting millions of people worldwide. An anonymous dataset is used to train a neural network to evaluate hearing loss in prevention and early diagnosis with the aim of supporting health care by optimising time and cost. The system is tested using a second set of data and results in a correct evaluation of whether the patient is affected by hearing loss or not.
Calabrese, Samuele; Donati, Eugenio; Chousidis, Christos
Affiliation: University of West London
AES Convention: 148 (May 2020) Paper Number: 10373
Publication Date: May 28, 2020
Subject: Signal Processing
No AES members have commented on this paper yet.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.