Literature DB >> 29230589

Physical parameter estimation from porcine ex vivo vocal fold dynamics in an inverse problem framework.

Pablo Gómez1, Anne Schützenberger2, Stefan Kniesburges2, Christopher Bohr3, Michael Döllinger2.   

Abstract

This study presents a framework for a direct comparison of experimental vocal fold dynamics data to a numerical two-mass-model (2MM) by solving the corresponding inverse problem of which parameters lead to similar model behavior. The introduced 2MM features improvements such as a variable stiffness and a modified collision force. A set of physiologically sensible degrees of freedom is presented, and three optimization algorithms are compared on synthetic vocal fold trajectories. Finally, a total of 288 high-speed video recordings of six excised porcine larynges were optimized to validate the proposed framework. Particular focus lay on the subglottal pressure, as the experimental subglottal pressure is directly comparable to the model subglottal pressure. Fundamental frequency, amplitude and objective function values were also investigated. The employed 2MM is able to replicate the behavior of the porcine vocal folds very well. The model trajectories' fundamental frequency matches the one of the experimental trajectories in [Formula: see text] of the recordings. The relative error of the model trajectory amplitudes is on average [Formula: see text]. The experiments feature a mean subglottal pressure of 10.16 (SD [Formula: see text]) [Formula: see text]; in the model, it was on average 7.61 (SD [Formula: see text]) [Formula: see text]. A tendency of the model to underestimate the subglottal pressure is found, but the model is capable of inferring trends in the subglottal pressure. The average absolute error between the subglottal pressure in the model and the experiment is 2.90 (SD [Formula: see text]) [Formula: see text] or [Formula: see text]. A detailed analysis of the factors affecting the accuracy in matching the subglottal pressure is presented.

Keywords:  High-speed videoendoscopy; Inverse problem; Two-mass model; Vocal fold oscillation

Mesh:

Year:  2017        PMID: 29230589     DOI: 10.1007/s10237-017-0992-5

Source DB:  PubMed          Journal:  Biomech Model Mechanobiol        ISSN: 1617-7940


  5 in total

1.  Estimation of vocal fold physiology from voice acoustics using machine learning.

Authors:  Zhaoyan Zhang
Journal:  J Acoust Soc Am       Date:  2020-03       Impact factor: 1.840

2.  Bayesian estimation of vocal function measures using laryngeal high-speed videoendoscopy and glottal airflow estimates: An in vivo case study.

Authors:  Gabriel A Alzamendi; Rodrigo Manríquez; Paul J Hadwin; Jonathan J Deng; Sean D Peterson; Byron D Erath; Daryush D Mehta; Robert E Hillman; Matías Zañartu
Journal:  J Acoust Soc Am       Date:  2020-05       Impact factor: 1.840

3.  Voice Feature Selection to Improve Performance of Machine Learning Models for Voice Production Inversion.

Authors:  Zhaoyan Zhang
Journal:  J Voice       Date:  2021-04-10       Impact factor: 2.300

4.  Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model.

Authors:  Emiro J Ibarra; Jesús A Parra; Gabriel A Alzamendi; Juan P Cortés; Víctor M Espinoza; Daryush D Mehta; Robert E Hillman; Matías Zañartu
Journal:  Front Physiol       Date:  2021-09-01       Impact factor: 4.566

5.  Rethinking glottal midline detection.

Authors:  Andreas M Kist; Julian Zilker; Pablo Gómez; Anne Schützenberger; Michael Döllinger
Journal:  Sci Rep       Date:  2020-11-26       Impact factor: 4.379

  5 in total

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