Preprint
A deep-learning based generalized reduced-order model of glottal flow during normal phonation
arXiv (Cornell University)
05/22/2020
DOI: 10.48550/arxiv.2005.11427
Abstract
This paper proposes a deep-learning based generalized reduced-order model
(ROM) that can provide a fast and accurate prediction of the glottal flow
during normal phonation. The approach is based on the assumption that the
vibration of the vocal folds can be represented by a universal kinematics
equation (UKE), which is used to generate a glottal shape library. For each
shape in the library, the ground truth values of the flow rate and pressure
distribution are obtained from the high-fidelity Navier-Stokes (N-S) solution.
A fully-connected deep neural network (DNN)is then trained to build the
empirical mapping between the shapes and the flow rate and pressure
distributions. The obtained DNN based reduced-order flow solver is coupled with
a finite-element method (FEM) based solid dynamics solver for FSI simulation of
phonation. The reduced-order model is evaluated by comparing to the
Navier-Stokes solutions in both statics glottal shaps and FSI simulations. The
results demonstrate a good prediction performance in accuracy and efficiency.
Details
- Title: Subtitle
- A deep-learning based generalized reduced-order model of glottal flow during normal phonation
- Creators
- Yang ZhangWeili JiangLuning SunJianxun WangSimeon SmithIngo R TitzeXudong ZhengQian Xue
- Resource Type
- Preprint
- Publication Details
- arXiv (Cornell University)
- DOI
- 10.48550/arxiv.2005.11427
- Language
- English
- Date posted
- 05/22/2020
- Academic Unit
- Communication Sciences and Disorders; School of Music
- Record Identifier
- 9984719735402771
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