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.

Authors: Calabrese, Samuele; Donati, Eugenio; Chousidis, Christos
Affiliation: University of West London
AES Convention: 148 (May 2020) Paper Number: 10373
Publication Date: May 28, 2020 Import into BibTeX
Subject: Signal Processing
Permalink: http://www.aes.org/e-lib/browse.cfm?elib=20790

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