Titulo:
Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal
.
Sumario:
Introducción. Los trastornos laríngeos se caracterizan por un cambio en el patrón vibratorio de los pliegues vocales. Este trastorno puede tener un origen orgánico, descrito como la modificación anatómica de los pliegues vocales, o de origen funcional, provocado por abuso o mal uso de la voz. Los métodos de diagnóstico más comunes se realizan mediante procedimientos invasivos que causan malestar al paciente. Además, los desvíos vocales de grado leve no impiden que el individuo utilice la voz, lo que dificulta la identificación del problema y aumenta la posibilidad de complicaciones futuras. Objetivo. Por esas razones, el objetivo de esta investigación es desarrollar una herramienta alternativa, no invasiva para la identificación de voces co... Ver más
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FUNDACION UNIVERSITARIA MARIA CANO |
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Colombia |
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Revista de Investigación e Innovación en Ciencias de la Salud |
| title |
Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal |
| spellingShingle |
Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal Morikawa, Mateus Spatti, Danilo Hernane Dajer, María Eugenia Trastorno de la voz Clasificación de voz Desviación de voz Red neuronal artificial Perceptron Multicamadas Transformada Wavelet Packet Afonía Enfermedades laríngeas Cuerdas vocales Voice Voice disorder Voice classification Voice Deviation Artificial Neural Network Multilayer Perceptron Wavelet Packet Transform Dysphonia Laryngeal Diseases Vocal Cords |
| title_short |
Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal |
| title_full |
Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal |
| title_fullStr |
Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal |
| title_full_unstemmed |
Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal |
| title_sort |
transformada wavelet packet y perceptrón multicapa para identificación de voces con grado leve de desvío vocal |
| description |
Introducción. Los trastornos laríngeos se caracterizan por un cambio en el patrón vibratorio de los pliegues vocales. Este trastorno puede tener un origen orgánico, descrito como la modificación anatómica de los pliegues vocales, o de origen funcional, provocado por abuso o mal uso de la voz. Los métodos de diagnóstico más comunes se realizan mediante procedimientos invasivos que causan malestar al paciente. Además, los desvíos vocales de grado leve no impiden que el individuo utilice la voz, lo que dificulta la identificación del problema y aumenta la posibilidad de complicaciones futuras.
Objetivo. Por esas razones, el objetivo de esta investigación es desarrollar una herramienta alternativa, no invasiva para la identificación de voces con grado leve de desvío vocal aplicando Transformada Wavelet Packet (WPT) y la red neuronal artificial del tipo Perceptrón Mutlicapa (PMC).
Métodos. Fue utilizado un banco de datos con 78 voces. Fueron extraídas las medidas de energía y entropía de Shannon usando las familias Daubechies 2 y Symlet 2 para después aplicar la red neuronal PMC.
Resultados. La familia Symlet 2 fue más eficiente en su generalización, obteniendo un 99.75% y un 99.56% de precisión mediante el uso de medidas de energía y entropía de Shannon, respectivamente. La familia Daubechies 2, sin embargo, obtuvo menores índices de precisión: 91.17% y 70.01%, respectivamente.
Conclusión. La combinación de WPT y PMC presentó alta precisión para la identificación de voces con grado leve de desvío vocal.
|
| description_eng |
Introduction. Laryngeal disorders are characterized by a change in the vibratory pattern of the vocal folds. This disorder may have an organic origin described by anatomical fold modification, or a functional origin caused by vocal abuse or misuse. The most common diagnostic methods are performed by invasive imaging features that cause patient discomfort. In addition, mild voice deviations do not stop the individual from using their voices, which makes it difficult to identify the problem and increases the possibility of complications.
Aim. For those reasons, the goal of the present paper was to develop a noninvasive alternative for the identification of voices with a mild degree of vocal deviation applying the Wavelet Packet Transform (WPT) and Multilayer Perceptron (MLP), an Artificial Neural Network (ANN).
Methods. A dataset of 74 audio files were used. Shannon energy and entropy measures were extracted using the Daubechies 2 and Symlet 2 families and then the processing step was performed with the MLP ANN.
Results. The Symlet 2 family was more efficient in its generalization, obtaining 99.75% and 99.56% accuracy by using Shannon energy and entropy measures, respectively. The Daubechies 2 family, however, obtained lower accuracy rates: 91.17% and 70.01%, respectively.
Conclusion. The combination of WPT and MLP presented high accuracy for the identification of voices with a mild degree of vocal deviation.
|
| author |
Morikawa, Mateus Spatti, Danilo Hernane Dajer, María Eugenia |
| author_facet |
Morikawa, Mateus Spatti, Danilo Hernane Dajer, María Eugenia |
| topicspa_str_mv |
Trastorno de la voz Clasificación de voz Desviación de voz Red neuronal artificial Perceptron Multicamadas Transformada Wavelet Packet Afonía Enfermedades laríngeas Cuerdas vocales |
| topic |
Trastorno de la voz Clasificación de voz Desviación de voz Red neuronal artificial Perceptron Multicamadas Transformada Wavelet Packet Afonía Enfermedades laríngeas Cuerdas vocales Voice Voice disorder Voice classification Voice Deviation Artificial Neural Network Multilayer Perceptron Wavelet Packet Transform Dysphonia Laryngeal Diseases Vocal Cords |
| topic_facet |
Trastorno de la voz Clasificación de voz Desviación de voz Red neuronal artificial Perceptron Multicamadas Transformada Wavelet Packet Afonía Enfermedades laríngeas Cuerdas vocales Voice Voice disorder Voice classification Voice Deviation Artificial Neural Network Multilayer Perceptron Wavelet Packet Transform Dysphonia Laryngeal Diseases Vocal Cords |
| citationvolume |
4 |
| citationissue |
1 |
| publisher |
Fundación Universitaria María Cano |
| ispartofjournal |
Revista de Investigación e Innovación en Ciencias de la Salud |
| source |
https://riics.info/index.php/RCMC/article/view/126 |
| language |
eng |
| format |
Article |
| rights |
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es Revista de Investigación e Innovación en Ciencias de la Salud - 2022 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references_eng |
Imamura R, Tsuji DH, Sennes LU. Fisiologia da laringe. In Pinho S, Tsuji DH, Bohadana S, editors. Fundamentos de Laringologia e Voz. 1st ed. Rio de Janeiro: Revinter Ltda; 2006. 2. Behlau M, Rocha B, Englert M, Madazio G. Validation of the Brazilian Portuguese CAPE-V Instrument—Br CAPE-V for Auditory-Perceptual Analysis. J Voice. 2020. doi: https://doi.org/10.1016/j.jvoice.2020.07.007 3. Patel S, Shrivastav R. Perception of dysphonic vocal quality: some thoughts and research update. Perspect Voice Voice Dis. 2007;17:3–6. doi: https://doi.org/10.1044/vvd17.2.3 4. Eadie T, Sroka A, Wright DR, Merati A. Does knowledge of medical diagnosis bias auditory-perceptual judgments of dysphonia? J Voice. 2011;25:420–429. doi: https://doi.org/10.1016/j.jvoice.2009.12.009 5. Yamasaki R, Madazio G, Leão SHS, Padovani M, Azevedo R, Behlau M. Auditory-perceptual Evaluation of Normal and Dysphonic Voices Using the Voice Deviation Scale. J Voice. 2016;31:67-71. doi: https://doi.org/10.1016/j.jvoice.2016.01.004 6. Webb AL, Carding PN, Deary IJ, MacKenzie K, Steen N, Wilson JA. The reliability of three perceptual evaluation scales for dysphonia. Eur Arch Otorhinolaryngol. 2004;261:429-434. doi: https://doi.org/10.1007/s00405-003-0707-7 7. Karnell MP, Melton SD, Childes JM, Coleman T, Dailey S, Hoffman H. Reliability of clinician-based (GRBAS and CAPE-V) and patient-based (V-RQOL and IPVI) documentation of voice disorders. J Voice. 2007;21:576-590. doi: https://doi.org/10.1016/j.jvoice.2006.05.001 8. Kempster GB, Gerratt BR, Verdolini Abbott K, Barkmeier-Karemer J, Hillman RE. Consensus auditory-perceptual evaluation of voice: development of a standardized clinical protocol. Am J Speech Lang Pathol. 2009;18:124-132. doi: https://doi.org/10.1044/1058-0360(2008/08-0017) 9. Tan BT, Fu M, Spray A, Dermody P. The use of wavelet transforms in phoneme recognition. Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96; 1996 Out 3 – Out 6; Philadelphia, USA. IEEE; 2002. p. 2431-2434. doi: https://doi.org/10.1109/ICSLP.1996.607300 10. Lima AAM, Barros FKH, Yoshizumi VH, Spatti DH, Dajer ME. Optimized Artificial Neural Network for Biosignals Classification Using Genetic Algorithm. J Control Autom Electr. 2019;30:371-379. doi: https://doi.org/10.1007/s40313-019-00454-1 11. Oliveira HM. Análise de Fourier e Wavelets: Sinais Estacionários e não Estacionários. Recife: Editora Universitária, UFPE; 2007. 12. Jiao S, Shi W, Liu Q. Self-adaptative partial discharge denoising based on variation mode decomposition and wavelet packet transform. Chinese automation congress; 2017 Out 20 – Out 22; Jinan, China. IEEE; 2018 Jan. p. 6. doi: https://doi.org/10.3390/en12173242. 13. Ramirez-Villegas JF, Ramirez-Moreno DF. Wavelet packet Energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. J Neurocomputing. 2012;77(1):82-100. doi: https://doi.org/10.1016/j.neucom.2011.08.015. 14. Zhang Y, Dong Z, Wang S, Ji G, Yang J. Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). J Entropy. 2015;17(4):1795-1813. doi: https://doi.org/10.3390/e17041795 15. Barizão H, Fermino MA, Dajer ME, Liboni LHB, Spatti DH. Voice disorder classification using MLP and wavelet packet transform. 2018 International Joint Conference on Neural Networks (IJCNN); 2018 Jul 8 – Jul 13; Rio de Janeiro, Brazil; IEEE; 2018. p. 8. doi: https://doi.org/10.1109/IJCNN.2018.8489121 16. Alves M, Silva G, Bispo BC, Dajer ME, Rodrigues PM. Voice Disorders Detection Through Multiband Cepstral Features of Sustained Vowel. J Voice. 2021;35(5):1-10. doi: https://doi.org/10.1016/j.jvoice.2021.01.018 17. Silva IND, Spatti DH, Flauzino RA. Redes Neurais Artificiais para engenharia e ciências aplicadas. São Paulo: Artliber; 2010. 18. Haykin S. Redes Neurais: Princípios e Prática. 2nd ed. Hamilton: Bookman; 2001. 19. Souzanchi-K M, Owhadi-Kareshk M, Akbarzadeh-T MR. Control of elastic joint robot based on electromyogram signal by pre-trained Multi- Layer Perceptron. 2016 International Joint Conference on Neural Networks (IJCNN); 2016 Jul 24 – Jul 29; Vancouver, Canada; IEEE; 2016. doi: https://doi.org/10.1109/IJCNN.2016.7727891 20. Baracho SF, Pinheiro DJLL, de Melo VV, Coelho RC. A hybrid neural system for the automatic segmentation of the interventricular septum in echocardiographic images. 2016 International Joint Conference on Neural Networks (IJCNN); 2016 Jul 24 – Jul 29; Vancouver, Canada; IEEE; 2016. doi: https://doi.org/10.1109/IJCNN.2016.7727868 21. Bevilacqua V, Salatino AA, Di Leo C, Tatolli G, Buongiorno D, Signorile D, et al. Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers. 2015 International Joint Conference on Neural Networks (IJCNN); 2015 Jul 12 – Jul 17; Killarney, Ireland; IEEE; 2015. doi: https://doi.org/10.1109/IJCNN.2015.7280463 22. Silva EHD, Morikawa M, Suterio VB, et al. Aplicação De Rede Neural Artificial Especialista Em Reconhecimento De Transtornos Vocais Moderados. In: Dallamuta J, Ajuz Holzman H, organizers. Engenharia Elétrica: Comunicação Integrada no Universo da Energia. 1st ed. Ponta Grossa: Atena Editora; 2021. doi: https://doi.org/10.22533/at.ed.3732123021 23. MATLAB. version 9.3 (R2017b). Natick, Massachusetts: The MathWorks Inc.; 2017. 24. Zambon FC. Estratégias de enfrentamento em professores com queixa de voz. [thesis]. [São Paulo]: Universidade Federal de São Paulo; 2011. 25. Paliwal KK, Lyons JG, Wójcicki KK. Preference for 20 40 ms window duration in speech analysis. 2010 4th International Conference on Signal Processing and Communication Systems; 2010 Dec 13 – Dec 15; Gold Coast, Austrália; IEEE; 2011. doi: https://doi.org/10.1109/ICSPCS.2010.5709770 26. Lima AAM. Classificação de Disfonias Utilizando Redes Neurais Artificiais e Transformadas Wavelet Packet. [Bachelor’s thesis]. [Cornélio Procópio]: Universidade Tecnológica Federal do Paraná; 2018. 27. Lever J, Krzywinski M, Altman N. Classification evaluation. Nat Methods. 2016;13:603–604. doi: https://doi.org/10.1038/nmeth.3945. 28. Medeiros JdaSA, Santos SMM, Teixeira LC, Cortes Gama AC, de Medeiros AM. Sintomas vocais relatados por professoras com disfonia e fatores associados. J Audiol Commun Res. 2016;21:1-8. doi: https://doi.org/10.1590/2317-6431-2015-1553 29. Giannini SSP, Ferreira LP. Voice disorders in teachers and the International Classification of Functioning, Disability and Health (ICF). Rev. Investig. Innov. Cienc. Salud [Internet]. 2021 Aug. 3 [cited 2022 Feb. 5];3(1):33-47. doi: https://doi.org/10.46634/riics.60 30. Cantor-Cutiva LC, Cuervo-Diaz DE, Hunter EJ, Moreno-Angarita M. Impairment, disability, and handicap associated with hearing problems and voice disorders among Colombian teachers. Rev. Investig. Innov. Cienc. Salud [Internet]. 2021 Aug. 3 [cited 2022 Feb. 5];3(1):4-21. doi: https://doi.org/10.46634/riics.48 |
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Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal Introducción. Los trastornos laríngeos se caracterizan por un cambio en el patrón vibratorio de los pliegues vocales. Este trastorno puede tener un origen orgánico, descrito como la modificación anatómica de los pliegues vocales, o de origen funcional, provocado por abuso o mal uso de la voz. Los métodos de diagnóstico más comunes se realizan mediante procedimientos invasivos que causan malestar al paciente. Además, los desvíos vocales de grado leve no impiden que el individuo utilice la voz, lo que dificulta la identificación del problema y aumenta la posibilidad de complicaciones futuras. Objetivo. Por esas razones, el objetivo de esta investigación es desarrollar una herramienta alternativa, no invasiva para la identificación de voces con grado leve de desvío vocal aplicando Transformada Wavelet Packet (WPT) y la red neuronal artificial del tipo Perceptrón Mutlicapa (PMC). Métodos. Fue utilizado un banco de datos con 78 voces. Fueron extraídas las medidas de energía y entropía de Shannon usando las familias Daubechies 2 y Symlet 2 para después aplicar la red neuronal PMC. Resultados. La familia Symlet 2 fue más eficiente en su generalización, obteniendo un 99.75% y un 99.56% de precisión mediante el uso de medidas de energía y entropía de Shannon, respectivamente. La familia Daubechies 2, sin embargo, obtuvo menores índices de precisión: 91.17% y 70.01%, respectivamente. Conclusión. La combinación de WPT y PMC presentó alta precisión para la identificación de voces con grado leve de desvío vocal. Introduction. Laryngeal disorders are characterized by a change in the vibratory pattern of the vocal folds. This disorder may have an organic origin described by anatomical fold modification, or a functional origin caused by vocal abuse or misuse. The most common diagnostic methods are performed by invasive imaging features that cause patient discomfort. In addition, mild voice deviations do not stop the individual from using their voices, which makes it difficult to identify the problem and increases the possibility of complications. Aim. For those reasons, the goal of the present paper was to develop a noninvasive alternative for the identification of voices with a mild degree of vocal deviation applying the Wavelet Packet Transform (WPT) and Multilayer Perceptron (MLP), an Artificial Neural Network (ANN). Methods. A dataset of 74 audio files were used. Shannon energy and entropy measures were extracted using the Daubechies 2 and Symlet 2 families and then the processing step was performed with the MLP ANN. Results. The Symlet 2 family was more efficient in its generalization, obtaining 99.75% and 99.56% accuracy by using Shannon energy and entropy measures, respectively. The Daubechies 2 family, however, obtained lower accuracy rates: 91.17% and 70.01%, respectively. Conclusion. The combination of WPT and MLP presented high accuracy for the identification of voices with a mild degree of vocal deviation. Morikawa, Mateus Spatti, Danilo Hernane Dajer, María Eugenia Voz Trastorno de la voz Clasificación de voz Desviación de voz Red neuronal artificial Perceptron Multicamadas Transformada Wavelet Packet Afonía Enfermedades laríngeas Cuerdas vocales Voice Voice disorder Voice classification Voice Deviation Artificial Neural Network Multilayer Perceptron Wavelet Packet Transform Dysphonia Laryngeal Diseases Vocal Cords 4 1 Artículo de revista Journal article 2022-02-05T00:00:00Z 2022-02-05T00:00:00Z 2022-02-05 application/pdf text/xml Fundación Universitaria María Cano Revista de Investigación e Innovación en Ciencias de la Salud 2665-2056 https://riics.info/index.php/RCMC/article/view/126 10.46634/riics.126 https://doi.org/10.46634/riics.126 eng https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es Revista de Investigación e Innovación en Ciencias de la Salud - 2022 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. 16 25 Imamura R, Tsuji DH, Sennes LU. Fisiologia da laringe. In Pinho S, Tsuji DH, Bohadana S, editors. Fundamentos de Laringologia e Voz. 1st ed. Rio de Janeiro: Revinter Ltda; 2006. 2. Behlau M, Rocha B, Englert M, Madazio G. Validation of the Brazilian Portuguese CAPE-V Instrument—Br CAPE-V for Auditory-Perceptual Analysis. J Voice. 2020. doi: https://doi.org/10.1016/j.jvoice.2020.07.007 3. Patel S, Shrivastav R. Perception of dysphonic vocal quality: some thoughts and research update. Perspect Voice Voice Dis. 2007;17:3–6. doi: https://doi.org/10.1044/vvd17.2.3 4. Eadie T, Sroka A, Wright DR, Merati A. Does knowledge of medical diagnosis bias auditory-perceptual judgments of dysphonia? J Voice. 2011;25:420–429. doi: https://doi.org/10.1016/j.jvoice.2009.12.009 5. Yamasaki R, Madazio G, Leão SHS, Padovani M, Azevedo R, Behlau M. Auditory-perceptual Evaluation of Normal and Dysphonic Voices Using the Voice Deviation Scale. J Voice. 2016;31:67-71. doi: https://doi.org/10.1016/j.jvoice.2016.01.004 6. Webb AL, Carding PN, Deary IJ, MacKenzie K, Steen N, Wilson JA. The reliability of three perceptual evaluation scales for dysphonia. Eur Arch Otorhinolaryngol. 2004;261:429-434. doi: https://doi.org/10.1007/s00405-003-0707-7 7. Karnell MP, Melton SD, Childes JM, Coleman T, Dailey S, Hoffman H. Reliability of clinician-based (GRBAS and CAPE-V) and patient-based (V-RQOL and IPVI) documentation of voice disorders. J Voice. 2007;21:576-590. doi: https://doi.org/10.1016/j.jvoice.2006.05.001 8. Kempster GB, Gerratt BR, Verdolini Abbott K, Barkmeier-Karemer J, Hillman RE. Consensus auditory-perceptual evaluation of voice: development of a standardized clinical protocol. Am J Speech Lang Pathol. 2009;18:124-132. doi: https://doi.org/10.1044/1058-0360(2008/08-0017) 9. Tan BT, Fu M, Spray A, Dermody P. The use of wavelet transforms in phoneme recognition. Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96; 1996 Out 3 – Out 6; Philadelphia, USA. IEEE; 2002. p. 2431-2434. doi: https://doi.org/10.1109/ICSLP.1996.607300 10. Lima AAM, Barros FKH, Yoshizumi VH, Spatti DH, Dajer ME. Optimized Artificial Neural Network for Biosignals Classification Using Genetic Algorithm. J Control Autom Electr. 2019;30:371-379. doi: https://doi.org/10.1007/s40313-019-00454-1 11. Oliveira HM. Análise de Fourier e Wavelets: Sinais Estacionários e não Estacionários. Recife: Editora Universitária, UFPE; 2007. 12. Jiao S, Shi W, Liu Q. Self-adaptative partial discharge denoising based on variation mode decomposition and wavelet packet transform. Chinese automation congress; 2017 Out 20 – Out 22; Jinan, China. IEEE; 2018 Jan. p. 6. doi: https://doi.org/10.3390/en12173242. 13. Ramirez-Villegas JF, Ramirez-Moreno DF. Wavelet packet Energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. J Neurocomputing. 2012;77(1):82-100. doi: https://doi.org/10.1016/j.neucom.2011.08.015. 14. Zhang Y, Dong Z, Wang S, Ji G, Yang J. Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). J Entropy. 2015;17(4):1795-1813. doi: https://doi.org/10.3390/e17041795 15. Barizão H, Fermino MA, Dajer ME, Liboni LHB, Spatti DH. Voice disorder classification using MLP and wavelet packet transform. 2018 International Joint Conference on Neural Networks (IJCNN); 2018 Jul 8 – Jul 13; Rio de Janeiro, Brazil; IEEE; 2018. p. 8. doi: https://doi.org/10.1109/IJCNN.2018.8489121 16. Alves M, Silva G, Bispo BC, Dajer ME, Rodrigues PM. Voice Disorders Detection Through Multiband Cepstral Features of Sustained Vowel. J Voice. 2021;35(5):1-10. doi: https://doi.org/10.1016/j.jvoice.2021.01.018 17. Silva IND, Spatti DH, Flauzino RA. Redes Neurais Artificiais para engenharia e ciências aplicadas. São Paulo: Artliber; 2010. 18. Haykin S. Redes Neurais: Princípios e Prática. 2nd ed. Hamilton: Bookman; 2001. 19. Souzanchi-K M, Owhadi-Kareshk M, Akbarzadeh-T MR. Control of elastic joint robot based on electromyogram signal by pre-trained Multi- Layer Perceptron. 2016 International Joint Conference on Neural Networks (IJCNN); 2016 Jul 24 – Jul 29; Vancouver, Canada; IEEE; 2016. doi: https://doi.org/10.1109/IJCNN.2016.7727891 20. Baracho SF, Pinheiro DJLL, de Melo VV, Coelho RC. A hybrid neural system for the automatic segmentation of the interventricular septum in echocardiographic images. 2016 International Joint Conference on Neural Networks (IJCNN); 2016 Jul 24 – Jul 29; Vancouver, Canada; IEEE; 2016. doi: https://doi.org/10.1109/IJCNN.2016.7727868 21. Bevilacqua V, Salatino AA, Di Leo C, Tatolli G, Buongiorno D, Signorile D, et al. Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers. 2015 International Joint Conference on Neural Networks (IJCNN); 2015 Jul 12 – Jul 17; Killarney, Ireland; IEEE; 2015. doi: https://doi.org/10.1109/IJCNN.2015.7280463 22. Silva EHD, Morikawa M, Suterio VB, et al. Aplicação De Rede Neural Artificial Especialista Em Reconhecimento De Transtornos Vocais Moderados. In: Dallamuta J, Ajuz Holzman H, organizers. Engenharia Elétrica: Comunicação Integrada no Universo da Energia. 1st ed. Ponta Grossa: Atena Editora; 2021. doi: https://doi.org/10.22533/at.ed.3732123021 23. MATLAB. version 9.3 (R2017b). Natick, Massachusetts: The MathWorks Inc.; 2017. 24. Zambon FC. Estratégias de enfrentamento em professores com queixa de voz. [thesis]. [São Paulo]: Universidade Federal de São Paulo; 2011. 25. Paliwal KK, Lyons JG, Wójcicki KK. Preference for 20 40 ms window duration in speech analysis. 2010 4th International Conference on Signal Processing and Communication Systems; 2010 Dec 13 – Dec 15; Gold Coast, Austrália; IEEE; 2011. doi: https://doi.org/10.1109/ICSPCS.2010.5709770 26. Lima AAM. Classificação de Disfonias Utilizando Redes Neurais Artificiais e Transformadas Wavelet Packet. [Bachelor’s thesis]. [Cornélio Procópio]: Universidade Tecnológica Federal do Paraná; 2018. 27. Lever J, Krzywinski M, Altman N. Classification evaluation. Nat Methods. 2016;13:603–604. doi: https://doi.org/10.1038/nmeth.3945. 28. Medeiros JdaSA, Santos SMM, Teixeira LC, Cortes Gama AC, de Medeiros AM. Sintomas vocais relatados por professoras com disfonia e fatores associados. J Audiol Commun Res. 2016;21:1-8. doi: https://doi.org/10.1590/2317-6431-2015-1553 29. Giannini SSP, Ferreira LP. Voice disorders in teachers and the International Classification of Functioning, Disability and Health (ICF). Rev. Investig. Innov. Cienc. 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