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Article

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Title

Dynamic Processing Neural Network Architecture for Hearing Loss Compensation

Authors

[ 1 ] Instytut Automatyki i Robotyki, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technologies

Year of publication

2024

Published in

IEEE/ACM Transactions on Audio, Speech, and Language Processing

Journal year: 2024 | Journal volume: vol. 32

Article type

scientific article

Publication language

english

Abstract

EN This paper proposes neural networks for compensating sensorineural hearing loss. The aim of the hearing loss compensation task is to transform a speech signal to increase speech intelligibility after further processing by a person with a hearing impairment, which is modeled by a hearing loss model. We propose an interpretable model called dynamic processing network, which has a structure similar to band-wise dynamic compressor. The network is differentiable, and therefore allows to learn its parameters to maximize speech intelligibility. More generic models based on convolutional layers were tested as well. The performance of the tested architectures was assessed using spectro-temporal objective index (STOI) with hearing-threshold noise and hearing aid speech intelligibility (HASPI) metrics. The dynamic processing network gave a significant improvement of STOI and HASPI in comparison to popular compressive gain prescription rule Camfit. A large enough convolutional network could outperform the interpretable model with the cost of larger computational load. Finally, a combination of the dynamic processing network with convolutional neural network gave the best results in terms of STOI and HASPI.

Date of online publication

30.10.2023

Pages (from - to)

203 - 214

DOI

10.1109/TASLP.2023.3328285

URL

https://ieeexplore.ieee.org/abstract/document/10301523

Ministry points / journal

140

Impact Factor

4,1 [List 2023]

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