M Döllinger1, S Kniesburges2, M Kaltenbacher3, M Echternach4. 1. Abteilung für Phoniatrie und Pädaudiologie an der HNO-Klinik Erlangen, Universitätsklinikum Erlangen, Bohlenplatz 21, 91054, Erlangen, Deutschland. michael.doellinger@uk-erlangen.de. 2. Abteilung für Phoniatrie und Pädaudiologie an der HNO-Klinik Erlangen, Universitätsklinikum Erlangen, Bohlenplatz 21, 91054, Erlangen, Deutschland. 3. Institute of Mechanics and Mechatronics/E325-A4, Vienna University of Technology, Getreidemarkt 9, 1060, Wien, Österreich. 4. Institut für Musikermedizin, Uniklinik Freiburg, Breisacher Str. 60, 79102, Freiburg, Deutschland.
Abstract
BACKGROUND: Many details of the phonatory process are not yet fully understood. Besides observational research, scientists have long since been trying to explain the physical fundamentals of voicing using simulations. This approach is commonly called modeling. However, the knowledge gained often failed to find its way to professionals working with the voice, such as singing teachers, voice therapists, and voice coaches, and sometimes also to otorhinolaryngologists and phoniatricians. The reason for this is that scientific publications on this topic mostly contain very detailed mathematical and physical descriptions, which are often hard to understand. OBJECTIVE: A simplified presentation and explanation of current methods for modeling the phonatory process, which have contributed greatly to uncovering and understanding the relationships involved in voicing during recent years. METHODS: The presented methods cover a wide spectrum, ranging from numerically rather simple to mathematically highly complex models. Experimental models are based on self-oscillating silicon or static vocal folds. Cadaver models have the advantage of being representative of the natural phonation process. RESULTS: An overview of different kinds of models is given to show the diversity of modeling approaches without going into mathematical or physical details. CONCLUSION: Numerical and experimental models for simulating the phonatory process enable causalities and correlations to be uncovered, which can be used in the future to adapt conservative and surgical voice therapies, or even to suggest entire new treatment strategies.
BACKGROUND: Many details of the phonatory process are not yet fully understood. Besides observational research, scientists have long since been trying to explain the physical fundamentals of voicing using simulations. This approach is commonly called modeling. However, the knowledge gained often failed to find its way to professionals working with the voice, such as singing teachers, voice therapists, and voice coaches, and sometimes also to otorhinolaryngologists and phoniatricians. The reason for this is that scientific publications on this topic mostly contain very detailed mathematical and physical descriptions, which are often hard to understand. OBJECTIVE: A simplified presentation and explanation of current methods for modeling the phonatory process, which have contributed greatly to uncovering and understanding the relationships involved in voicing during recent years. METHODS: The presented methods cover a wide spectrum, ranging from numerically rather simple to mathematically highly complex models. Experimental models are based on self-oscillating silicon or static vocal folds. Cadaver models have the advantage of being representative of the natural phonation process. RESULTS: An overview of different kinds of models is given to show the diversity of modeling approaches without going into mathematical or physical details. CONCLUSION: Numerical and experimental models for simulating the phonatory process enable causalities and correlations to be uncovered, which can be used in the future to adapt conservative and surgical voice therapies, or even to suggest entire new treatment strategies.
Authors: Clemens Kirmse; Michael Triep; C Brücker; Michael Döllinger; Michael Stingl Journal: Logoped Phoniatr Vocol Date: 2010-04 Impact factor: 1.487
Authors: Georg Luegmair; Stefan Kniesburges; Maik Zimmermann; Alexander Sutor; Ulrich Eysholdt; Michael Döllinger Journal: IEEE Trans Med Imaging Date: 2010-12 Impact factor: 10.048
Authors: Stefan Kniesburges; Scott L Thomson; Anna Barney; Michael Triep; Petr Sidlof; Jaromír Horáčcek; Christoph Brücker; Stefan Becker Journal: Curr Bioinform Date: 2011-09-01 Impact factor: 3.543
Authors: Michael Döllinger; James Kobler; David A Berry; Daryush D Mehta; Georg Luegmair; Christopher Bohr Journal: Curr Bioinform Date: 2011 Impact factor: 3.543
Authors: Michael Döllinger; Pablo Gómez; Rita R Patel; Christoph Alexiou; Christopher Bohr; Anne Schützenberger Journal: PLoS One Date: 2017-11-09 Impact factor: 3.240
Authors: Fabian Thornton; Michael Döllinger; Stefan Kniesburges; David Berry; Christoph Alexiou; Anne Schützenberger Journal: Appl Sci (Basel) Date: 2019-05-13 Impact factor: 2.679