Laryngeal Pressure Estimation With a Recurrent Neural Network.
High-speed video
inverse problem
recurrent neural networks
vocal fold dynamics
voice physiology
Journal
IEEE journal of translational engineering in health and medicine
ISSN: 2168-2372
Titre abrégé: IEEE J Transl Eng Health Med
Pays: United States
ID NLM: 101623153
Informations de publication
Date de publication:
2019
2019
Historique:
received:
09
07
2018
revised:
24
10
2018
accepted:
30
11
2018
entrez:
26
1
2019
pubmed:
27
1
2019
medline:
27
1
2019
Statut:
epublish
Résumé
Quantifying the physical parameters of voice production is essential for understanding the process of phonation and can aid in voice research and diagnosis. As an alternative to invasive measurements, they can be estimated by formulating an inverse problem using a numerical forward model. However, high-fidelity numerical models are often computationally too expensive for this. This paper presents a novel approach to train a long short-term memory network to estimate the subglottal pressure in the larynx at massively reduced computational cost using solely synthetic training data. We train the network on synthetic data from a numerical two-mass model and validate it on experimental data from 288 high-speed
Identifiants
pubmed: 30680252
doi: 10.1109/JTEHM.2018.2886021
pii: 2000111
pmc: PMC6331197
doi:
Types de publication
Journal Article
Langues
eng
Pagination
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