Fine-Tuning of a Voice Production Model to Estimate Impact Stress Using a Metaheuristic Method

Ajuste fino de un modelo de producción vocal para estimar el estrés de impacto utilizando un método metaheurístico

Published 2024-01-29 — Updated on 2024-02-03

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Vol. 6 No. 1 (2024)
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Research Article
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Fine-Tuning of a Voice Production Model to Estimate Impact Stress Using a Metaheuristic Method. Rev. Investig. Innov. Cienc. Salud [Internet]. 2024 Feb. 3 [cited 2024 Dec. 21];6(1):24-43. Available from: https://riics.info/index.php/RCMC/article/view/234
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https://doi.org/10.46634/riics.234
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Carlos-Alberto Calvache-Mora
Department of Mechatronics Engineering; Universidad Militar Nueva Granada; Bogotá, Colombia. / Vocology Research; Vocology Center; Bogotá; Colombia. / Communication Science and Disorders; Corporación Universitaria Iberoamericana; Bogotá; Colombia.
    https://orcid.org/0000-0002-5403-1852
    Leonardo Soláque
    Department of Mechatronics Engineering; Universidad Militar Nueva Granada; Bogotá, Colombia
      https://orcid.org/0000-0002-2773-1028
      Alexandra Velasco
      Department of Mechatronics Engineering; Universidad Militar Nueva Granada; Bogotá, Colombia
        https://orcid.org/0000-0001-7786-880X
        Lina Peñuela
        Department of Mechatronics Engineering; Universidad Militar Nueva Granada; Bogotá, Colombia
          https://orcid.org/0000-0002-1925-9296
          Show authors biography

          Introduction. In vocal production models employing spring-mass-damper frameworks, precision in determining damping coefficients that align with physiological vocal fold characteristics is crucial, accounting for potential variations in the representation of viscosity-elasticity properties.

          Objective. This study aims to conduct a parametric fitting of a vocal production model based on a mass-spring-damper system incorporating subglottic pressure interaction, with the purpose of accurately modeling the collision forces exerted by vocal folds during phonation.

          Method. A metaheuristic search algorithm was employed for parametric synthesis. The algorithm was applied to elasticity coefficients c1 and c2, as well as damping coefficients ε1 and ε2, which directly correlate with the mass matrices of the model. This facilitates the adjustment of fold composition to achieve desired physiological behavior.

          Results. The vocal system's behavior for each simulation cycle was compared to a predefined standard under normal conditions. The algorithm determined the simulation endpoint by evaluating discrepancies between key features of the obtained signals and the desired ones.

          Conclusion. Parametric fitting enabled the approximation of physiological vocal production behavior, providing estimates of the impact forces experienced by vocal folds during phonation.

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          1. Zhang Y, Zheng X, Xue Q. A Deep Neural Network Based Glottal Flow Model for Predicting Fluid-Structure Interactions during Voice Production. Appl Sci [Internet]. 2020 Jan 19;10(2):1-18. doi: https://doi.org/10.3390/app10020705
          2. Titze IR. Nonlinear source–filter coupling in phonation: Theory. J Acoust Soc Am [Internet]. 2008;123(5):2733-49. doi: https://doi.org/10.1121/1.2832337
          3. Hunter EJ, Titze IR, Alipour F. A three-dimensional model of vocal fold abduction/adduction. J Acoust Soc Am [Internet]. 2004;115(4):1747-59. doi: https://doi.org/10.1121/1.1652033
          4. Story BH. An overview of the physiology, physics and modeling of the sound source for vowels. Acoust Sci Technol [Internet]. 2002;23(4):195-206. doi: https://doi.org/10.1250/ast.23.195
          5. Alipour F, Vigmostad S. Measurement of vocal folds elastic properties for continuum modeling. J Voice [Internet]. 2012;26(6):816.e21-816.e29. doi: https://doi.org/10.1016/j.jvoice.2012.04.010
          6. Berry DA, Zhang Z, Neubauer J. Mechanisms of irregular vibration in a physical model of the vocal folds. J Acoust Soc Am [Internet]. 2006;120(3):EL36-42. doi: https://doi.org/10.1121/1.2234519
          7. Delebecque L, Pelorson X, Beautemps D. Modeling of aerodynamic interaction between vocal folds and vocal tract during production of a vowel–voiceless plosive–vowel sequence. J Acoust Soc Am [Internet]. 2016;139(1):350-60. doi: https://doi.org/10.1121/1.4939115 :
          8. Šidlof P, Švec JG, Horáček J, Veselý J, Klepáček I, Havlík R. Geometry of human vocal folds and glottal channel for mathematical and biomechanical modeling of voice production. J Biomech [Internet]. 2008;41(5):985-95. doi: https://doi.org/10.1016/j.jbiomech.2007.12.016
          9. Zhang Z. Cause-effect relationship between vocal fold physiology and voice production in a three-dimensional phonation model. J Acoust Soc Am [Internet]. 2016;139(4):1493-507. doi: https://doi.org/10.1121/1.4944754
          10. Calvache C, Solaque L, Velasco A, Peñuela L. Biomechanical Models to Represent Vocal Physiology: A Systematic Review. J Voice [Internet]. 2023;37(3):465.e1-465.e18. doi: https://doi.org/10.1016/j.jvoice.2021.02.014
          11. Story BH, Titze IR. Voice simulation with a body-cover model of the vocal folds. J Acoust Soc Am [Internet]. 1995;97(2):1249-60. doi: https://doi.org/10.1121/1.412234
          12. Sadeghi H, Kniesburges S, Kaltenbacher M, Schützenberger A, Döllinger M. Computational Models of Laryngeal Aerodynamics: Potentials and Numerical Costs. J Voice [Internet]. 2019;33(4):385-400. doi: https://doi.org/10.1016/j.jvoice.2018.01.001
          13. Erath BD, Zañartu M, Peterson SD. Modeling viscous dissipation during vocal fold contact: the influence of tissue viscosity and thickness with implications for hydration. Biomech Model Mechanobiol [Internet]. 2017;16(3):947-60. doi: https://doi.org/10.1007/s10237-016-0863-5
          14. Espinoza VM, Zañartu M, Van Stan JH, Mehta DD, Hillman RE. Glottal aerodynamic measures in women with phonotraumatic and nonphonotraumatic vocal hyperfunction. J Speech Lang Hear Res [Internet]. 2017;60(8):2159-69. doi: https://doi.org/10.1044/2017_JSLHR-S-16-0337
          15. Hadwin PJ, Galindo GE, Daun KJ, Zañartu M, Erath BD, Cataldo E, et al. Non-stationary Bayesian estimation of parameters from a body cover model of the vocal folds. J Acoust Soc Am [Internet]. 2016;139(5):2683-96. doi: https://doi.org/10.1121/1.4948755
          16. Horáček J, Laukkanen AM, Šidlof P. Estimation of impact stress using an aeroelastic model of voice production. Logop Phoniatr Vocology [Internet]. 2007;32(4):185-92. doi: https://doi.org/10.1080/14015430600628039
          17. Horacek J, Laukkanen A-M, Sidlof P, Murphy P, Svec JG. Comparison of Acceleration and Impact Stress as Possible Loading Factors in Phonation: A Computer Modeling Study. Folia Phoniatr Logop [Internet]. 2009;61(3):137-45. doi: https://doi.org/10.1159/000219949
          18. Hillman RE, Stepp CE, Van Stan JH, Zañartu M, Mehta DD. An updated theoretical framework for vocal hyperfunction. Am J Speech-Language Pathol [Internet]. 2020;29(4):2254–60. doi: https://doi.org/10.1044/2020_AJSLP-20-00104
          19. Cortés JP, Espinoza VM, Ghassemi M, Mehta DD, Van Stan JH, Hillman RE, et al. Ambulatory assessment of phonotraumatic vocal hyperfunction using glottal airflow measures estimated from neck-surface acceleration. PLoS One [Internet]. 2018;13(12):1-23. doi: https://doi.org/10.1371/journal.pone.0209017
          20. Schwarz R, Huttner B, Döllinger M, Luegmair G, Eysholdt U, Schuster M, et al. Substitute Voice Production: Quantification of PE Segment Vibrations Using a Biomechanical Model. IEEE Trans Biomed Eng [Internet]. 2011;58(10):2767-76. doi: https://doi.org/10.1109/tbme.2011.2151860
          21. Šidlof P, Zörner S, Hüppe A. A hybrid approach to the computational aeroacoustics of human voice production. Biomech Model Mechanobiol [Internet]. 2015;14(3):473-88. doi: https://doi.org/10.1007/s10237-014-0617-1
          22. Neumaier A. Complete search in continuous global optimization and constraint satisfaction. Acta Numer [Internet]. 2004;13:271-369. doi: https://doi.org/10.1017/s0962492904000194
          23. Elaziz MA, Elsheikh AH, Oliva D, Abualigah L, Lu S, Ewees AA. Advanced Metaheuristic Techniques for Mechanical Design Problems: Review. Arch Comput Methods Eng [Internet]. 2021;29:695-716. doi: https://doi.org/10.1007/s11831-021-09589-4
          24. Li H-L, Chang C-T, Tsai J-F. Approximately global optimization for assortment problems using piecewise linearization techniques. Eur J Oper Res [Internet]. 2002;140(3):584-9. doi: https://doi.org/10.1016/s0377-2217(01)00194-1
          25. Pinkevich V, Oppacher F. Platunov A. Model-driven functional testing of cyber-physical systems using deterministic replay techniques. In: 2018 IEEE Industrial Cyber-Physical Systems (ICPS) [Internet]. 2018 May 15-18; Saint Petersburg, Russia: IEEE; 2018. p. 141-6. doi: https://doi.org/10.1109/icphys.2018.8387650
          26. Tsai J-F. Global optimization for signomial discrete programming problems in engineering design. Eng Optim [Internet]. 2010;42(9):833-43. doi: https://doi.org/10.1080/03052150903456485
          27. Lin M-H, Tsai J-F, Yu C-S. A Review of Deterministic Optimization Methods in Engineering and Management. Math Probl Eng [Internet]. 2012;2012:1-15. doi: https://doi.org/10.1155/2012/756023
          28. Ecker JG, Kupferschmid M, Lawrence CE, Reilly AA, Scott ACH. An application of nonlinear optimization in molecular biology. Eur J Oper Res [Internet]. 2002;138(2):452-8. doi: https://doi.org/10.1016/s0377-2217(01)00122-9
          29. Bertuzzi A, Conte F, Papa F, Sinisgalli C. Applications of Nonlinear Programming to the Optimization of Fractionated Protocols in Cancer Radiotherapy. Information [Internet]. 2020;11(6):1-24. doi: https://doi.org/10.3390/info11060313
          30. Jiang H, Olleta B, Chen D, Geiger R. Parameter optimization of deterministic dynamic element matching DACs for accurate and cost-effective ADC testing. In: Proceedings of 2004 International Symposium on Circuits and Systems [Internet]. 2004 May 23-26; Vancouver, Canada: IEEE; 2004. p. 924-927. doi: https://doi.org/10.1109/iscas.2004.1328347
          31. Tameemi AQ. Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model. IEEE Access [Internet]. 2020;8:193005-19. doi: https://doi.org/10.1109/access.2020.3033497
          32. Fang J, Lin S, Xu Z. Learning Through Deterministic Assignment of Hidden Parameters. IEEE Trans Cybern [Internet]. 2020 May;50(5):2321-34. doi: https://doi.org/10.1109/tcyb.2018.2885029
          33. Bandaru S, Deb K. Metaheuristic Techniques. In: Decision Sciences [Internet]. Boca Raton: CRC Press; 2016. p. 693-750. doi: https://doi.org/10.1201/9781315183176
          34. Worch E, Samiappan S, Zhou M, Ball JE. Hyperspectral Band Selection Using Moth-Flame Metaheuristic Optimization. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 [Internet]. 2020 Sep 26-Oct 2; Waikoloa, USA: IEEE; 2020. p. 1271-4 doi: https://doi.org/10.1109/igarss39084.2020.9323754
          35. Altay EV, Alatas B. Music based metaheuristic methods for constrained optimization. In: Varol A, Karabatak M, Varol C, editors. 6th International Symposium on Digital Forensic and Security Proceedings [Internet]. 2018 Mar 22-25; Antalya, Turkey: IEEE; 2018. p. 222-7. doi: https://doi.org/10.1109/isdfs.2018.8355355
          36. Kurniasih J, Utami E, Raharjo S. Heuristics and Metaheuristics Approach for Query Optimization Using Genetics and Memetics Algorithm. In: 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS) [Internet]. 2019 Aug 22-23; Bali, Indonesia: Institut Teknologi dan Bisnis (ITB); 2019. p. 168-72. doi: https://doi.org/10.1109/icoris.2019.8874909
          37. Gokalp O, Ugur A. An order based hybrid metaheuristic algorithm for solving optimization problems. In: 2nd International Conference on Computer Science and Engineering (UBMK) [Internet]. 2017 Oct 5-8; Antalya, Turkey: IEEE; 2017. p. 604-9. doi: https://doi.org/10.1109/ubmk.2017.8093477
          38. Yang A, Stingl M, Berry DA, Lohscheller J, Voigt D, Eysholdt U, et al. Computation of physiological human vocal fold parameters by mathematical optimization of a biomechanical model. J Acoust Soc Am [Internet]. 2011;130(2):948-64. doi: https://doi.org/10.1121/1.3605551
          39. Prom-on S, Birkholz P, Xu Y. Estimating vocal tract shapes of Thai vowels from contextual vowel variation. In: 2014 17th Oriental Chapter of the International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques (COCOSDA) [Internet]. 2014 Sep 10-12; Phuket, Thailand: IEEE; 2014. p. 1-6. doi: https://doi.org/10.1109/icsda.2014.7051442
          40. Dognin P, El-Jaroudi A, Billa J. Parameter optimization for vocal tract length normalization. In: 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings (Cat No00CH37100). Vol 3. [Internet]. 2000 Jun 5-9; Istanbul, Turkey: IEEE; 2000. p. 1767-70. doi: https://doi.org/10.1109/icassp.2000.862095
          41. Laprie Y, Mathieu B. A variational approach for estimating vocal tract shapes from the speech signal. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ‘98 (Cat No98CH36181). Vol 2. [Internet]. 1998 May 12-15; Seattle, USA: IEEE; 1998. p. 929-32. doi: https://doi.org/10.1109/icassp.1998.675418
          42. Ding W, Campbell N, Higuchi N, Kasuya H. Fast and robust joint estimation of vocal tract and voice source parameters. In: 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing. Vol 2. [Internet]. 1997 Apr 21-24; Munich, Germany: IEEE; 1997. p. 1291-4. doi: https://doi.org/10.1109/icassp.1997.596182
          43. Villalba Fernández de Castro G, Saldarriaga GJ. Algoritmos de Optimización Combinatoria (AOC) aplicados al diseño de redes de distribución de agua potable. Rev Ing [Internet]. 2005;(22):118-25. Available from: https://ojsrevistaing.uniandes.edu.co/ojs/index.php/revista/article/view/393
          44. Bernstein DJ. Understanding brute force. [Internet]. 2005 Available from: https://cr.yp.to/snuffle/bruteforce-20050425.pdf
          45. Mohammad A, Saleh O, Abdeen RA. Occurrences Algorithm for String Searching Based on Brute-force Algorithm. J Comput Sci [Internet]. 2006;2(1):82-5. Available from: https://thescipub.com/abstract/jcssp.2006.82.85
          46. Rao SS. Metaheuristic Optimization Methods. In: Engineering Optimization Theory and Practice [Internet]. New York: Wiley; 2019. p. 673-95. doi: https://doi.org/10.1002/9781119454816.ch14
          47. Radhika S, Chaparala A. Optimization using evolutionary metaheuristic techniques: a brief review. Brazilian J Oper & Prod Manag [Internet]. 2018;15(1):44-53. doi: https://doi.org/10.14488/bjopm.2018.v15.n1.a17
          48. Horáček J, Šidlof P, Švec JG. Numerical simulation of self-oscillations of human vocal folds with Hertz model of impact forces. J Fluids Struct. 2005;20(6):853-69. doi: https://doi.org/10.1016/j.jfluidstructs.2005.05.003
          49. Stronge WJ. Impact Mechanics [Internet]. Cambridge: Cambridge University Press; 2000. 280 p. doi: https://doi.org/10.1017/cbo9780511626432
          50. Půst L, Peterka F. Impact oscillator with Hertz’s model of contact. Meccanica [Internet]. 2003;38(1):99-116. doi: https://doi.org/10.1023/a:1022075519038
          51. Suman B, Kumar P. A survey of simulated annealing as a tool for single and multiobjective optimization. J Oper Res Soc [Internet]. 2006;57(10):1143-60. doi: https://doi.org/10.1057/palgrave.jors.2602068
          52. Caballero-Villalobos JP, Alvarado-Valencia JA. Greedy Randomized Adaptive Search Procedure (GRASP), una alternativa valiosa en la minimización de la tardanza total ponderada en una máquina. Ing y Univ [Internet]. 2010;14(2):275-95. Available from: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0123-21262010000200004&nrm=iso
          53. Hoos H, Sttzle T. Stochastic Local Search: Foundations & Applications. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2004. 658 p.
          54. Palaparthi A, Riede T, Titze IR. Combining Multiobjective Optimization and Cluster Analysis to Study Vocal Fold Functional Morphology. IEEE Trans Biomed Eng [Internet]. 2014;61(7):2199-208. doi: https://doi.org/10.1109/TBME.2014.2319194
          55. Idrisoglu A, Dallora AL, Anderberg P, Berglund JS. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. J Med Internet Res [Internet]. 2023 Jul;25:e46105. doi: https://doi.org/10.2196/46105
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