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
Subject:
Signal Processing
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