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

Abstract

For the clinical analysis of underlying mechanisms of voice disorders, we developed a numerical aeroacoustic larynx model, called simVoice, that mimics commonly observed functional laryngeal disorders as glottal insufficiency and vibrational left-right asymmetries. The model is a combination of the Finite Volume (FV) CFD solver Star-CCM+ and the Finite Element (FE) aeroacoustic solver CFS++. simVoice models turbulence using Large Eddy Simulations (LES) and the acoustic wave propagation with the perturbed convective wave equation (PCWE). Its geometry corresponds to a simplified larynx and a vocal tract model representing the vowel /a/. The oscillations of the vocal folds are externally driven. In total, 10 configurations with different degrees of functional-based disorders were simulated and analyzed. The energy transfer between the glottal airflow and the vocal folds decreases with an increasing glottal insufficiency and potentially reflects the higher effort during speech for patients being concerned. This loss of energy transfer may also have an essential influence on the quality of the sound signal as expressed by decreasing sound pressure level (SPL), Cepstral Peak Prominence (CPP), and Vocal Efficiency (VE). Asymmetry in the vocal fold oscillations also reduces the quality of the sound signal. However, simVoice confirmed previous clinical and experimental observations that a high level of glottal insufficiency worsens the acoustic signal quality more than oscillatory left-right asymmetry. Both symptoms in combination will further reduce the quality of the sound signal. In summary, simVoice allows for detailed analysis of the origins of disordered voice production and hence fosters the further understanding of laryngeal physiology, including occurring dependencies. A current walltime of 10 h/cycle is, with a prospective increase in computing power, auspicious for a future clinical use of simVoice.

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

“…A promising approach for a screening tool fulfilling these requirements could be based on bioacoustic signals such as speech sounds or cough sounds (Brown et al, 2020;Hecker et al, 2021;. Several studies have reported acoustic peculiarities in the speech of patients who have diseases associated with symptoms affecting anatomical correlates of speech production, such as bronchial asthma (Balamurali et al, 2020;Dogan et al, 2007) or vocal cord disorders (Falk et al, 2021;Jesus et al, 2015;Petrović-Lazić et al, 2011). Differences in various acoustic parameters were also found in recent studies comparing speech samples of COVID-19 positive and COVID-19 negative individuals (Asiaee et al, 2020;Bartl-Pokorny et al, 2021).…”

“…Although research on the automatic detection of diseases based on speech is rapidly expanding, it faces a number of challenges in terms of algorithm generalisability and potential application in real-world scenarios. These challenges include gender and age distribution, the presence of different mother tongues, dialects, sociolects, or cognitive aspects such as individual speech-language and reading competence that may affect various acoustic parameters (Alves et al, 2015;Goyal et al, 2021;Nagumo et al, 2020;Procter & Joshi, 2020;Rojas et al, 2020;Sun, 2020;Taylor et al, 2020). Studies on COVID-19 face additional challenges related to the fact that COVID-19 is a relatively new and not yet well understood disease with a wide range of symptoms and divergent symptom severity (Hu et al, 2020;Tu et al, 2020).…”

Efficient numerical simulation of the human voice

“…The detailed description of the model with focus on the numerical efficiency is provided in [37], whereas a thorough description of the CFD incompressible flow simulation can be found in [31] and [30]. Furthermore, an extensive source term analysis for a validated synthetic setup is provided in [36] and the application to typical vocal cord dysfunctions is presented in [7].…”

Finite-element modeling of vocal fold self-oscillations in interaction with vocal tract: Comparison of incompressible and compressible flow model

“…Radiation to an open space was not considered in this study, similarly as in other studies [19,43,60,62,83,86,87]. The PML layer of elements used in some models [24,63] could not be applied here because the PML layer is designated for acoustical elements rather than the elements for fluid flow simulations.…”

“…Many authors created new models of phonation with a prescribed movement of the VF and the acoustic analogy or APE. They showed that the dominant acoustic source is the sound generated by the flow through the glottis [9,34,58,85] and the prescribed (driven kinematic) VF movement is reasonable simplification resulting in lower computational costs [24,50,51,63,87]. Yet Alipour et al upgraded their model [2,4] with the self-oscillating VFs and found that the distributions along the centerline axis in this model were similar as in [19,27,52,69] and the presented formants of the vowels [a:] and [i:] were in good correspondence to those observed in humans.…”