Fluid-structure-acoustic interactions in an <i>ex vivo</i> porcine phonation model

Semmler, Marion; Berry, David A.; Schützenberger, Anne; Döllinger, Michael

doi:10.1121/10.0003602

Cited by 13 publications

(10 citation statements)

References 65 publications

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“…With increasing backcoupling, the SNR increases even more, leading to an improved voice quality in these cases. The HNR values reported are in the range of values found for ex vivo studies in the literature [55,56]. They are, however, at the lower end of what is normally found in vivo [57][58][59].…”

Section: Acoustic Radiationsupporting

confidence: 48%

An Investigation of Acoustic Back-Coupling in Human Phonation on a Synthetic Larynx Model

Näger,

Kniesburges,

Tur

et al. 2023

Bioengineering

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In the human phonation process, acoustic standing waves in the vocal tract can influence the fluid flow through the glottis as well as vocal fold oscillation. To investigate the amount of acoustic back-coupling, the supraglottal flow field has been recorded via high-speed particle image velocimetry (PIV) in a synthetic larynx model for several configurations with different vocal tract lengths. Based on the obtained velocity fields, acoustic source terms were computed. Additionally, the sound radiation into the far field was recorded via microphone measurements and the vocal fold oscillation via high-speed camera recordings. The PIV measurements revealed that near a vocal tract resonance frequency fR, the vocal fold oscillation frequency fo (and therefore also the flow field’s fundamental frequency) jumps onto fR. This is accompanied by a substantial relative increase in aeroacoustic sound generation efficiency. Furthermore, the measurements show that fo-fR-coupling increases vocal efficiency, signal-to-noise ratio, harmonics-to-noise ratio and cepstral peak prominence. At the same time, the glottal volume flow needed for stable vocal fold oscillation decreases strongly. All of this results in an improved voice quality and phonation efficiency so that a person phonating with fo-fR-coupling can phonate longer and with better voice quality.

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Section: Acoustic Radiationsupporting

confidence: 48%

An Investigation of Acoustic Back-Coupling in Human Phonation on a Synthetic Larynx Model

Näger,

Kniesburges,

Tur

et al. 2023

Bioengineering

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“…The investigation employed a multimodal measurement setup acquiring different physical parameters from the model. The setup used in this study is a modified version of the setup introduced by Birk [45] for ex vivo larynx models [46][47][48]. The synthetic larynx model was mounted on an artificial trachea with a diameter of 24 mm.…”

Section: Measurement Setup and Data Acquisitionmentioning

confidence: 99%

Effect of Ligament Fibers on Dynamics of Synthetic, Self-Oscillating Vocal Folds in a Biomimetic Larynx Model

Tur,

Gühring,

Wendler

et al. 2023

Bioengineering

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Synthetic silicone larynx models are essential for understanding the biomechanics of physiological and pathological vocal fold vibrations. The aim of this study is to investigate the effects of artificial ligament fibers on vocal fold vibrations in a synthetic larynx model, which is capable of replicating physiological laryngeal functions such as elongation, abduction, and adduction. A multi-layer silicone model with different mechanical properties for the musculus vocalis and the lamina propria consisting of ligament and mucosa was used. Ligament fibers of various diameters and break resistances were cast into the vocal folds and tested at different tension levels. An electromechanical setup was developed to mimic laryngeal physiology. The measurements included high-speed video recordings of vocal fold vibrations, subglottal pressure and acoustic. For the evaluation of the vibration characteristics, all measured values were evaluated and compared with parameters from ex and in vivo studies. The fundamental frequency of the synthetic larynx model was found to be approximately 200–520 Hz depending on integrated fiber types and tension levels. This range of the fundamental frequency corresponds to the reproduction of a female normal and singing voice range. The investigated voice parameters from vocal fold vibration, acoustics, and subglottal pressure were within normal value ranges from ex and in vivo studies. The integration of ligament fibers leads to an increase in the fundamental frequency with increasing airflow, while the tensioning of the ligament fibers remains constant. In addition, a tension increase in the fibers also generates a rise in the fundamental frequency delivering the physiological expectation of the dynamic behavior of vocal folds.

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“…Observed subglottal pressure values range from 459 Pa to 1494 Pa and are approximately (Lilliefors test, p = 0.946) normal distributed to

, and the sensor’s accuracy was about 35 Pa ( Gómez et al, 2019 ). The pre-phonatory configurations include symmetric ( Birk et al, 2017b ) and asymmetric ( Semmler et al, 2021 ) arytenoid torques (5–25 m Nm). Furthermore, the rest positions were affected by differing posterior gaps used in the setup: in 140 recordings, a 1 mm metal shim was inserted between arytenoid cartilages; in 95 recordings, a 2 mm shim was used, and the remaining 53 recordings were unmodified.…”

Section: Methodsmentioning

confidence: 99%

Neural network-based estimation of biomechanical vocal fold parameters

Donhauser,

Tur,

Döllinger

2024

Front. Physiol.

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Vocal fold (VF) vibrations are the primary source of human phonation. High-speed video (HSV) endoscopy enables the computation of descriptive VF parameters for assessment of physiological properties of laryngeal dynamics, i.e., the vibration of the VFs. However, underlying biomechanical factors responsible for physiological and disordered VF vibrations cannot be accessed. In contrast, physically based numerical VF models reveal insights into the organ’s oscillations, which remain inaccessible through endoscopy. To estimate biomechanical properties, previous research has fitted subglottal pressure-driven mass–spring–damper systems, as inverse problem to the HSV-recorded VF trajectories, by global optimization of the numerical model. A neural network trained on the numerical model may be used as a substitute for computationally expensive optimization, yielding a fast evaluating surrogate of the biomechanical inverse problem. This paper proposes a convolutional recurrent neural network (CRNN)-based architecture trained on regression of a physiological-based biomechanical six-mass model (6 MM). To compare with previous research, the underlying biomechanical factor “subglottal pressure” prediction was tested against 288 HSV ex vivo porcine recordings. The contributions of this work are two-fold: first, the presented CRNN with the 6 MM handles multiple trajectories along the VFs, which allows for investigations on local changes in VF characteristics. Second, the network was trained to reproduce further important biomechanical model parameters like VF mass and stiffness on synthetic data. Unlike in a previous work, the network in this study is therefore an entire surrogate of the inverse problem, which allowed for explicit computation of the fitted model using our approach. The presented approach achieves a best-case mean absolute error (MAE) of 133 Pa (13.9%) in subglottal pressure prediction with 76.6% correlation on experimental data and a re-estimated fundamental frequency MAE of 15.9 Hz (9.9%). In-detail training analysis revealed subglottal pressure as the most learnable parameter. With the physiological-based model design and advances in fast parameter prediction, this work is a next step in biomechanical VF model fitting and the estimation of laryngeal kinematics.

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Fluid-structure-acoustic interactions in an ex vivo porcine phonation model

Cited by 13 publications

References 65 publications

An Investigation of Acoustic Back-Coupling in Human Phonation on a Synthetic Larynx Model

An Investigation of Acoustic Back-Coupling in Human Phonation on a Synthetic Larynx Model

Effect of Ligament Fibers on Dynamics of Synthetic, Self-Oscillating Vocal Folds in a Biomimetic Larynx Model

Neural network-based estimation of biomechanical vocal fold parameters

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