3D-FV-FE Aeroacoustic Larynx Model for Investigation of Functional Based Voice Disorders

Restraining vocal fold vertical motion reduces source-filter interaction in a two-mass model

Previous experimental studies suggested that restraining the vocal fold vertical motion may reduce the coupling strength between the voice source and vocal tract. In this study, the effects of vocal fold vertical motion on source-filter interaction were systematically examined in a two-dimensional two-mass model coupled to a compressible flow simulation. The results showed that when allowed to move vertically, the vocal folds exhibited subharmonic vibration due to entrainment to the first vocal tract acoustic resonance. Restraining the vertical motion suppressed this entrainment. This indicates that the vertical mobility of the vocal folds may play a role in regulating source-filter interaction.

Neural network-based estimation of biomechanical vocal fold parameters

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.

Bilateral Functional Electrical Stimulation for the Treatment of Presbyphonia in a Sheep Model

OBJECTIVES

The aim of the study was to increase muscle volume and improve phonation characteristics of the aged ovine larynx by functional electrical stimulation (FES) using a minimally invasive surgical procedure.

METHODS

Stimulation electrodes were placed bilaterally near the terminal adduction branch of the recurrent laryngeal nerves (RLN). The electrodes were connected to battery powered pulse generators implanted subcutaneously at the neck region. Training patterns were programmed by an external programmer using a bidirectional radio frequency link. Training sessions were repeated automatically by the implant every other day for 1 week followed by every day for 8 weeks in the awake animal. Another group of animals were used as sham, with electrodes positioned but not connected to an implant. Outcome parameters included gene expression analysis, histological assessment of muscle fiber size, functional analysis, and volumetric measurements based on three-dimensional reconstructions of the entire thyroarytenoid muscle (TAM).

RESULTS

Increase in minimal muscle fiber diameter and an improvement in vocal efficiency were observed following FES, compared with sham animals.

CONCLUSION

This is the first study to demonstrate beneficial effects in the TAM of FES at molecular, histological, and functional levels. FES of the terminal branches of the RLN reversed the effects of age-related changes and improved vocal efficiency.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:848-854, 2024.

On the Alignment of Acoustic and Coupled Mechanic-Acoustic Eigenmodes in Phonation by Supraglottal Duct Variations

Sound generation in human phonation and the underlying fluid-structure-acoustic interaction that describes the sound production mechanism are not fully understood. A previous experimental study, with a silicone made vocal fold model connected to a straight vocal tract pipe of fixed length, showed that vibroacoustic coupling can cause a deviation in the vocal fold vibration frequency. This occurred when the fundamental frequency of the vocal fold motion was close to the lowest acoustic resonance frequency of the pipe. What is not fully understood is how the vibroacoustic coupling is influenced by a varying vocal tract length. Presuming that this effect is a pure coupling of the acoustical effects, a numerical simulation model is established based on the computation of the mechanical-acoustic eigenvalue. With varying pipe lengths, the lowest acoustic resonance frequency was adjusted in the experiments and so in the simulation setup. In doing so, the evolution of the vocal folds' coupled eigenvalues and eigenmodes is investigated, which confirms the experimental findings. Finally, it was shown that for normal phonation conditions, the mechanical mode is the most efficient vibration pattern whenever the acoustic resonance of the pipe (lowest formant) is far away from the vocal folds' vibration frequency. Whenever the lowest formant is slightly lower than the mechanical vocal fold eigenfrequency, the coupled vocal fold motion pattern at the formant frequency dominates.

Computational fluid dynamics of upper airway aerodynamics for exercise-induced laryngeal obstruction: A feasibility study

Objective

Use of computational fluid dynamic (CFD) simulations to measure the changes in upper airway geometry and aerodynamics during (a) an episode of Exercise-Induced Laryngeal Obstruction (EILO) and (b) speech therapy exercises commonly employed for patients with EILO.

Methods

Magnetic resonance imaging stills of the upper airway including the nasal and oral cavities from an adult female were used to re-construct three-dimensional geometries of the upper airway. The CFD simulations were used to compute the maximum volume flow rate (l/s), pressure (Pa), airflow velocity (m/s) and area of cross-section opening in eight planes along the vocal tract, separately for inhalation and exhalation.

Results

Numerical predictions from three-dimensional geometrical modeling of the upper airway suggest that the technique of nose breathing for inhalation and pursed lip breathing for exhalation show most promising pressure conditions and cross-sectional diameters for rescue breathing exercises. Also, if EILO is due to the constriction at the vocal fold level, then a quick sniff may also be a proper rescue inhalation exercise. EILO affects both the inspiratory and the expiratory phases of breathing.

Conclusions

A prior knowledge of the supraglottal aerodynamics and the corresponding upper airway geometry from CFD analysis has the potential to assist the clinician in choosing the most effective rescue breathing technique for optimal functional outcome of speech therapy intervention in patients with EILO and in understanding the pathophysiology of EILO on a case-by-case basis with future studies.

Level of Evidence

4.

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

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.

Anisotropic minimum dissipation subgrid-scale model in hybrid aeroacoustic simulations of human phonation

This article deals with large-eddy simulations of three-dimensional incompressible laryngeal flow followed by acoustic simulations of human phonation of five cardinal English vowels, /ɑ, æ, i, o, u/. The flow and aeroacoustic simulations were performed in OpenFOAM and in-house code openCFS, respectively. Given the large variety of scales in the flow and acoustics, the simulation is separated into two steps: (1) computing the flow in the larynx using the finite volume method on a fine moving grid with 2.2 million elements, followed by (2) computing the sound sources separately and wave propagation to the radiation zone around the mouth using the finite element method on a coarse static grid with 33 000 elements. The numerical results showed that the anisotropic minimum dissipation model, which is not well known since it is not available in common CFD software, predicted stronger sound pressure levels at higher harmonics, and especially at first two formants, than the wall-adapting local eddy-viscosity model. The model on turbulent flow in the larynx was employed and a positive impact on the quality of simulated vowels was found.

Effect of nodule size and stiffness on phonation threshold and collision pressures in a synthetic hemilaryngeal vocal fold model

Synthetic vocal fold (VF) replicas were used to explore the role of nodule size and stiffness on kinematic, aerodynamic, and acoustic measures of voiced speech production. Emphasis was placed on determining how changes in collision pressure may contribute to the development of phonotrauma. This was performed by adding spherical beads with different sizes and moduli of elasticity at the middle of the medial surface of synthetic silicone VF models, representing nodules of varying size and stiffness. The VF models were incorporated into a hemilaryngeal flow facility. For each case, self-sustained oscillations were investigated at the phonation threshold pressure. It was found that increasing the nodule diameter increased the open quotient, phonation threshold pressure, and phonation threshold flow rate. However, these values did not change considerably as a function of the modulus of elasticity of the nodule. Nevertheless, the ratio of collision pressure to subglottal pressure increased significantly for both increasing nodule size and stiffness. This suggests that over time, both growth in size and fibrosis of nodules will lead to an increasing cycle of compensatory vocal hyperfunction that accelerates phonotrauma.

Synthetic mucus for an ex vivo phonation setup: Creation, application, and effect on excised porcine larynges

Laryngeal mucus hydrates and lubricates the deformable tissue of the vocal folds and acts as a boundary layer with the airflow from the lungs. However, the effects of the mucus' viscoelasticity on phonation remain widely unknown and mucus has not yet been established in experimental procedures of voice research. In this study, four synthetic mucus samples were created on the basis of xanthan with focus on physiological frequency-dependent viscoelastic properties, which cover viscosities and elasticities over 2 orders of magnitude. An established ex vivo experimental setup was expanded by a reproducible and controllable application method of synthetic mucus. The application method and the suitability of the synthetic mucus samples were successfully verified by fluorescence evidence on the vocal folds even after oscillation experiments. Subsequently, the impact of mucus viscoelasticity on the oscillatory dynamics of the vocal folds, the subglottal pressure, and acoustic signal was investigated with 24 porcine larynges (2304 datasets). Despite the large differences of viscoelasticity, the phonatory characteristics remained stable with only minor statistically significant differences. Overall, this study increased the level of realism in the experimental setup for replication of the phonatory process enabling further research on pathological mucus and exploration of therapeutic options.

Comparison of one-dimensional and three-dimensional glottal flow models in left-right asymmetric vocal fold conditions

While the glottal flow is often simplified as one-dimensional (1D) in computational models of phonation to reduce computational costs, the 1D flow model has not been validated in left-right asymmetric vocal fold conditions, as often occur in both normal and pathological voice production. In this study, we performed three-dimensional (3D) and 1D flow simulations coupled to a two-mass model of adult male vocal folds and compared voice production at different degrees of left-right stiffness asymmetry. The flow and acoustic fields in 3D were obtained by solving the compressible Navier-Stokes equations using the volume penalization method with the moving vocal fold wall as an immersed boundary. Despite differences in the predicted flow pressure on vocal fold surface between the 1D and 3D flow models, the results showed reasonable agreement in vocal fold vibration patterns and selected voice outcome measures between the 1D and 3D models for the range of left-right asymmetric conditions investigated. This indicates that vocal fold properties play a larger role than the glottal flow in determining the overall pattern of vocal fold vibration and the produced voice, and the 1D flow simplification is sufficient in modeling phonation, at least for the simplified glottal geometry of this study.

Re-Training of Convolutional Neural Networks for Glottis Segmentation in Endoscopic High-Speed Videos

Endoscopic high-speed video (HSV) systems for visualization and assessment of vocal fold dynamics in the larynx are diverse and technically advancing. To consider resulting "concepts shifts" for neural network (NN)-based image processing, re-training of already trained and used NNs is necessary to allow for sufficiently accurate image processing for new recording modalities. We propose and discuss several re-training approaches for convolutional neural networks (CNN) being used for HSV image segmentation. Our baseline CNN was trained on the BAGLS data set (58,750 images). The new BAGLS-RT data set consists of additional 21,050 images from previously unused HSV systems, light sources, and different spatial resolutions. Results showed that increasing data diversity by means of preprocessing already improves the segmentation accuracy (mIoU + 6.35%). Subsequent re-training further increases segmentation performance (mIoU + 2.81%). For re-training, finetuning with dynamic knowledge distillation showed the most promising results. Data variety for training and additional re-training is a helpful tool to boost HSV image segmentation quality. However, when performing re-training, the phenomenon of catastrophic forgetting should be kept in mind, i.e., adaption to new data while forgetting already learned knowledge.

Error detection and filtering of incompressible flow simulations for aeroacoustic predictions of human voice

The presented filtering technique is proposed to detect errors and correct outliers inside the acoustic sources, respectively, the first time derivative of the incompressible pressure obtained from large eddy simulations with prescribed vocal fold motion using overlay mesh methods. Regarding the perturbed convective wave equation, the time derivative of the incompressible pressure is the primary sound source in the human phonation process. However, the incompressible pressure can be erroneous and have outliers when fulfilling the divergence-free constraint of the velocity field. This error is primarily occurring for non-conserving prescribed vocal fold motions. Therefore, the method based on a continuous stationary random process was designed to detect rare events in the time derivative of the pressure. The detected events are then localized and treated by a defined window function to increase their probability. As a consequence, the data quality of the non-linearly filtered data is enhanced significantly. Furthermore, the proposed method can also be used to assess convergence of the aeroacoustic source terms, and detect regions and time intervals, which show a non-converging behavior by an impulse-like structure.

The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

OBJECTIVES

The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds.

METHODS

By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions.

RESULTS

The experimental results demonstrate that a set of acoustic parameters of cough sounds, e.g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general automatic COVID-19 detection model performs significantly above chance level, i.e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201).

CONCLUSIONS

Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models.

The mechanisms of harmonic sound generation during phonation: A multi-modal measurement-based approach

Sound generation during voiced speech remains an open research topic because the underlying process within the human larynx is hardly accessible for direct measurements. In the present study, harmonic sound generation during phonation was investigated with a model that replicates the fully coupled fluid-structure-acoustic interaction (FSAI). The FSAI was captured using a multi-modal approach by measuring the flow and acoustic source fields based on particle image velocimetry, as well as the surface velocity of the vocal folds based on laser vibrometry and high-speed imaging. Strong harmonic sources were localized near the glottis, as well as further downstream, during the presence of the supraglottal jet. The strongest harmonic content of the vocal fold surface motion was verified for the area near the glottis, which directly interacts with the glottal jet flow. Also, the acoustic back-coupling of the formant frequencies onto the harmonic oscillation of the vocal folds was verified. These findings verify that harmonic sound generation is the result of a strong interrelation between the vocal fold motion, modulated flow field, and vocal tract geometry.