Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas

Este trabajo busca determinar la arquitectura de red neuronal que pueda estimar las asimetrías de fase y amplitud en una voz NPVH, utilizando un modelo sintético para simular el movimiento del tracto vocal. El resultado será una red neuronal entrenada para predecir estas asimetrías, facilitando la d...

Full description

Autores:
Rodriguez Rodriguez, Oscar Duván
Téllez Rincón, Santiago
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2023
Institución:
Universidad Militar Nueva Granada
Repositorio:
Repositorio UMNG
Idioma:
spa
OAI Identifier:
oai:repository.unimilitar.edu.co:10654/45757
Acceso en línea:
http://hdl.handle.net/10654/45757
Palabra clave:
REDES NEURALES (COMPUTADORES)
INTELIGENCIA ARTIFICIAL
NPVH
neural network
Redes neuronales
Voz hiperfuncional no fonotraumática
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UNIMILTAR2_d9f218e2f30bfb282a7edfcba4e77bb5
oai_identifier_str oai:repository.unimilitar.edu.co:10654/45757
network_acronym_str UNIMILTAR2
network_name_str Repositorio UMNG
repository_id_str
dc.title.spa.fl_str_mv Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
dc.title.translated.spa.fl_str_mv Implementation of an artificial neural network for the prediction of phase and amplitude asymmetries in the case of non-phonotraumatic hyperfunctional voices
title Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
spellingShingle Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
REDES NEURALES (COMPUTADORES)
INTELIGENCIA ARTIFICIAL
NPVH
neural network
Redes neuronales
Voz hiperfuncional no fonotraumática
title_short Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
title_full Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
title_fullStr Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
title_full_unstemmed Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
title_sort Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
dc.creator.fl_str_mv Rodriguez Rodriguez, Oscar Duván
Téllez Rincón, Santiago
dc.contributor.advisor.none.fl_str_mv Peñuela Calderon, Lina Maria
Solaque Guzmán, Leonardo Enrique
dc.contributor.author.none.fl_str_mv Rodriguez Rodriguez, Oscar Duván
Téllez Rincón, Santiago
dc.subject.lemb.spa.fl_str_mv REDES NEURALES (COMPUTADORES)
INTELIGENCIA ARTIFICIAL
topic REDES NEURALES (COMPUTADORES)
INTELIGENCIA ARTIFICIAL
NPVH
neural network
Redes neuronales
Voz hiperfuncional no fonotraumática
dc.subject.keywords.spa.fl_str_mv NPVH
neural network
dc.subject.proposal.spa.fl_str_mv Redes neuronales
Voz hiperfuncional no fonotraumática
description Este trabajo busca determinar la arquitectura de red neuronal que pueda estimar las asimetrías de fase y amplitud en una voz NPVH, utilizando un modelo sintético para simular el movimiento del tracto vocal. El resultado será una red neuronal entrenada para predecir estas asimetrías, facilitando la detección de patologías de voz de manera no invasiva.
publishDate 2023
dc.date.issued.none.fl_str_mv 2023-08-02
dc.date.accessioned.none.fl_str_mv 2024-02-14T15:05:06Z
dc.date.available.none.fl_str_mv 2024-02-14T15:05:06Z
dc.type.local.spa.fl_str_mv Tesis/Trabajo de grado - Monografía - Pregrado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.coar.*.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
format http://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10654/45757
dc.identifier.instname.spa.fl_str_mv instname:Universidad Militar Nueva Granada
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad Militar Nueva Granada
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.unimilitar.edu.co
url http://hdl.handle.net/10654/45757
identifier_str_mv instname:Universidad Militar Nueva Granada
reponame:Repositorio Institucional Universidad Militar Nueva Granada
repourl:https://repository.unimilitar.edu.co
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Jackson-Menaldi, MC. La voz normal. Buenos Aires: Editorial Médica Panamericana; 1992
Petty TL, Enright PL. Simple office spirometry for primary care practitioners National Lung Health Education Program Web site. 2005. (Consultado el 28/02/2006.) Disponible en: http://www.nlhep.org/resources-medical.html\#review
¿Cómo se diagnostica una disfonía? (s/f). Seorl.net. Recuperado el 19 de septiembre de 2022, de https://seorl.net/como-se-diagnostica-una-disfonia
Alzamendi, G. A., Peterson, S. D., Erath, B. D., Hillman, R. E., & Zañartu, M. (2021). Triangular body-cover model of the vocal folds with coordinated activation of five intrinsic laryngeal muscles with applications to vocal hyperfunction. arXiv preprint arXiv:2108.01115
Galindo GE, Peterson SD, Erath BD, et al. Modeling the pathophysiology of phonotraumatic vocal hyperfunction with a triangular glottal model of the vocal folds. J Speech Lang Hearing Res. 2017;60:2452–2471. https://doi.org/10.1044/2017\_jslhr-s-16-0412
Z. -H. Ling et al., "Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends," in IEEE Signal Processing Magazine, vol. 32, no. 3, pp. 35-52, May 2015, doi: 10.1109/MSP.2014.2359987
H. Ze, A. Senior and M. Schuster, "Statistical parametric speech synthesis using deep neural networks," 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 7962-7966, doi: 10.1109/ICASSP.2013.6639215.
A. Morales and J. I. Yuz, "Reduced order modeling for glottal airflow estimation using a Kalman smoother," 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), 2021, pp. 1-6, doi: 10.1109/ICAACCA51523.2021.9465282
Michael J. Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A. Roch, Sharon Gannot, and Charles-Alban Deledalle , "Machine learning in acoustics: Theory and applications", The Journal of the Acoustical Society of America 146, 3590-3628 (2019) https://doi.org/10.1121/1.5133944
L. A. F. Mendoza, E. Cataldo, M. Vellasco, M. A. Silva, A. D. O. Cañon and J. M. de Seixas, "Classification of voice aging using ANN and glottal signal parameters," 2010 IEEE ANDESCON, 2010, pp. 1-5, doi: 10.1109/ANDESCON.2010.5633362
Ibarra EJ, Parra JA, Alzamendi GA, Cortés JP, Espinoza VM, Mehta DD, Hillman RE, Zañartu M. Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model. Front Physiol. 2021 Sep 1;12:732244. doi: 10.3389/fphys.2021.732244. PMID: 34539451; PMCID: PMC8440844.
Y. Wang et al., “Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities,” Brain Sciences, vol. 10, no. 7, p. 442, Jul. 2020, doi: 10.3390/brainsci10070442.
N. Y. -H. Wang et al., "Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 184-195, 2021, doi: 10.1109/TNSRE.2020.3042655
Souza CL, Carvalho FM, Araujo TM, Reis EJ, Lima VM, Porto LA. Factors associated with vocal fold pathologies in teachers. Rev Saude Publica, 2011 Oct; 45 (5): 914-21
Jiménez Fandiño LH, Wuesthoff C, García-Reyes JC. Estado de los profesionales de la voz en Colombia. Acta otorrinolaringol cir cabeza cuello [Internet]. 31 de agosto de 2018 [citado 14 de marzo de 2022];40(2):120-7. Disponible en: https://www.revista.acorl.org.co/index.php/acorl/article/view/224
Asofono.co. Recuperado el 22 de agosto de 2022, de https://asofono.co/wp-content/uploads/2016/09/ICOV\_p58-63\_Reyes\_Prevalencia.pdf
G. Gidaye, J. Nirmal, K. Ezzine and M. Frikha, "Effective Detection of Voice Dysfunction Using Glottic Flow Descriptors," 2019 Third International Conference on Inventive Systems and Control (ICISC), 2019, pp. 307-312, doi: 10.1109/ICISC44355.2019.9036362.
Un dispositivo “wearable” para el seguimiento de pacientes con problemas en 22la voz. (2019, noviembre 18). Noticias de la Ciencia. https://noticiasdelaciencia.com/art/35318/un-dispositivo-wearable-para-el-seguimiento-de-pacientes-con-problemas-en-la-voz
Verdolini, K., Rosen, C. A., & Branski, R. C. (Eds.). (2006). Classification manual for voice disorders-I. Erlbaum
Hillman RE, Stepp CE, Van Stan JH, Zañartu M, Mehta DD. An Updated Theoretical Framework for Vocal Hyperfunction. Am J Speech Lang Pathol. 2020 Nov 12;29(4):2254-2260. doi: 10.1044/2020\_AJSLP-20-00104. Epub 2020 Oct 2. PMID: 33007164; PMCID: PMC8740570
Trastornos de la voz - Diagnóstico y tratamiento - Mayo Clinic. (2022). Retrieved 24 April2022, from https://www.mayoclinic.org/es-es/diseases-conditions/voice-disorders/diagnosis-treatment/drc-20353024
Carlos Calvache, Leonardo Solaque, Alexandra Velasco, Lina Peñuela, Biomechanical Models to Represent Vocal Physiology: A Systematic Review, Journal of Voice, 2021, , ISSN 0892-1997, https://doi.org/10.1016/j.jvoice.2021.02.014. (https://www.sciencedirect.com/science/article/pii/S0892199721000680
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Nueva 2 Titze IR. Phonation threshold pressure: a missing link in glottal aerodynamics. J Acoust Soc Am. 1992; 91: 2926-35
J. Z. Lin, V. M. Espinoza, K. L. Marks, M. Zañartu and D. D. Mehta, "Improved Subglottal Pressure Estimation From Neck-Surface Vibration in Healthy Speakers Producing Non-Modal Phonation," in IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 2, pp. 449-460, Feb. 2020, doi: 10.1109/JSTSP.2019.2959267
Zañartu, M., Ho, J.C., Mehta, D.D., Hillman, R.E., & Wodicka, G.R. (2013). Subglottal Impedance-Based Inverse Filtering of Voiced Sounds Using Neck Surface Acceleration. IEEE Transactions on Audio, Speech, and Language Processing, 21, 1929-1939
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J. H. Van Stan et al., "Differences in Daily Voice Use Measures Between Female Patients With Nonphonotraumatic Vocal Hyperfunction and Matched Controls", Journal of Speech, Language, and Hearing Research, vol. 64, n.º 5, pp. 1457–1470, mayo de 2021. Accedido el 8 de septiembre de 2022. [En línea]. Disponible: https://doi.org/10.1044/2021\_jslhr-20-00538
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K. P. Murphy, Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press, 2012.
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"Vocología y Metacomunicación". VocologyCenter. https://vocologycenter.com (accedido el 26 de septiembre de 2022)
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spelling Peñuela Calderon, Lina MariaSolaque Guzmán, Leonardo EnriqueRodriguez Rodriguez, Oscar DuvánTéllez Rincón, SantiagoIngeniero en Mecatrónica2024-02-14T15:05:06Z2024-02-14T15:05:06Z2023-08-02http://hdl.handle.net/10654/45757instname:Universidad Militar Nueva Granadareponame:Repositorio Institucional Universidad Militar Nueva Granadarepourl:https://repository.unimilitar.edu.coEste trabajo busca determinar la arquitectura de red neuronal que pueda estimar las asimetrías de fase y amplitud en una voz NPVH, utilizando un modelo sintético para simular el movimiento del tracto vocal. El resultado será una red neuronal entrenada para predecir estas asimetrías, facilitando la detección de patologías de voz de manera no invasiva.This work aims to determine the neural network architecture that can estimate phase and amplitude asymmetries in an NPVH voice, using a synthetic model to simulate the movement of the vocal tract. The result will be a neural network trained to predict these asymmetries, facilitating the detection of voice pathologies in a non-invasive manner.Pregradoapplicaction/pdfspahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternationalAcceso abiertohttp://purl.org/coar/access_right/c_abf2Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticasImplementation of an artificial neural network for the prediction of phase and amplitude asymmetries in the case of non-phonotraumatic hyperfunctional voicesREDES NEURALES (COMPUTADORES)INTELIGENCIA ARTIFICIALNPVHneural networkRedes neuronalesVoz hiperfuncional no fonotraumáticaTesis/Trabajo de grado - Monografía - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fIngeniería en MecatrónicaFacultad de IngenieríaUniversidad Militar Nueva GranadaJackson-Menaldi, MC. La voz normal. Buenos Aires: Editorial Médica Panamericana; 1992Petty TL, Enright PL. Simple office spirometry for primary care practitioners National Lung Health Education Program Web site. 2005. (Consultado el 28/02/2006.) Disponible en: http://www.nlhep.org/resources-medical.html\#review¿Cómo se diagnostica una disfonía? (s/f). Seorl.net. Recuperado el 19 de septiembre de 2022, de https://seorl.net/como-se-diagnostica-una-disfoniaAlzamendi, G. A., Peterson, S. D., Erath, B. D., Hillman, R. E., & Zañartu, M. (2021). Triangular body-cover model of the vocal folds with coordinated activation of five intrinsic laryngeal muscles with applications to vocal hyperfunction. arXiv preprint arXiv:2108.01115Galindo GE, Peterson SD, Erath BD, et al. Modeling the pathophysiology of phonotraumatic vocal hyperfunction with a triangular glottal model of the vocal folds. J Speech Lang Hearing Res. 2017;60:2452–2471. https://doi.org/10.1044/2017\_jslhr-s-16-0412Z. -H. Ling et al., "Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends," in IEEE Signal Processing Magazine, vol. 32, no. 3, pp. 35-52, May 2015, doi: 10.1109/MSP.2014.2359987H. Ze, A. Senior and M. Schuster, "Statistical parametric speech synthesis using deep neural networks," 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 7962-7966, doi: 10.1109/ICASSP.2013.6639215.A. Morales and J. I. Yuz, "Reduced order modeling for glottal airflow estimation using a Kalman smoother," 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), 2021, pp. 1-6, doi: 10.1109/ICAACCA51523.2021.9465282Michael J. Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A. Roch, Sharon Gannot, and Charles-Alban Deledalle , "Machine learning in acoustics: Theory and applications", The Journal of the Acoustical Society of America 146, 3590-3628 (2019) https://doi.org/10.1121/1.5133944L. A. F. Mendoza, E. Cataldo, M. Vellasco, M. A. Silva, A. D. O. Cañon and J. M. de Seixas, "Classification of voice aging using ANN and glottal signal parameters," 2010 IEEE ANDESCON, 2010, pp. 1-5, doi: 10.1109/ANDESCON.2010.5633362Ibarra EJ, Parra JA, Alzamendi GA, Cortés JP, Espinoza VM, Mehta DD, Hillman RE, Zañartu M. Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model. Front Physiol. 2021 Sep 1;12:732244. doi: 10.3389/fphys.2021.732244. PMID: 34539451; PMCID: PMC8440844.Y. Wang et al., “Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities,” Brain Sciences, vol. 10, no. 7, p. 442, Jul. 2020, doi: 10.3390/brainsci10070442.N. Y. -H. Wang et al., "Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 184-195, 2021, doi: 10.1109/TNSRE.2020.3042655Souza CL, Carvalho FM, Araujo TM, Reis EJ, Lima VM, Porto LA. Factors associated with vocal fold pathologies in teachers. Rev Saude Publica, 2011 Oct; 45 (5): 914-21Jiménez Fandiño LH, Wuesthoff C, García-Reyes JC. Estado de los profesionales de la voz en Colombia. Acta otorrinolaringol cir cabeza cuello [Internet]. 31 de agosto de 2018 [citado 14 de marzo de 2022];40(2):120-7. Disponible en: https://www.revista.acorl.org.co/index.php/acorl/article/view/224Asofono.co. Recuperado el 22 de agosto de 2022, de https://asofono.co/wp-content/uploads/2016/09/ICOV\_p58-63\_Reyes\_Prevalencia.pdfG. Gidaye, J. Nirmal, K. Ezzine and M. Frikha, "Effective Detection of Voice Dysfunction Using Glottic Flow Descriptors," 2019 Third International Conference on Inventive Systems and Control (ICISC), 2019, pp. 307-312, doi: 10.1109/ICISC44355.2019.9036362.Un dispositivo “wearable” para el seguimiento de pacientes con problemas en 22la voz. (2019, noviembre 18). Noticias de la Ciencia. https://noticiasdelaciencia.com/art/35318/un-dispositivo-wearable-para-el-seguimiento-de-pacientes-con-problemas-en-la-vozVerdolini, K., Rosen, C. A., & Branski, R. C. (Eds.). (2006). Classification manual for voice disorders-I. ErlbaumHillman RE, Stepp CE, Van Stan JH, Zañartu M, Mehta DD. An Updated Theoretical Framework for Vocal Hyperfunction. Am J Speech Lang Pathol. 2020 Nov 12;29(4):2254-2260. doi: 10.1044/2020\_AJSLP-20-00104. Epub 2020 Oct 2. 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(Capítulo 1.6 aborda funciones de pérdida y criterios de optimización)Calle 100ORIGINALRodriguezRodriguezOscarDuvanTellezRinconSantiago2023.pdfRodriguezRodriguezOscarDuvanTellezRinconSantiago2023.pdfTesisapplication/pdf4595083http://repository.unimilitar.edu.co/bitstream/10654/45757/1/RodriguezRodriguezOscarDuvanTellezRinconSantiago2023.pdf340a2ecf6104f9fa9a7ca9e6017ba0eaMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-83420http://repository.unimilitar.edu.co/bitstream/10654/45757/2/license.txta609d7e369577f685ce98c66b903b91bMD52open access10654/45757oai:repository.unimilitar.edu.co:10654/457572024-02-14 10:05:07.812open accessRepositorio Institucional 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