Modelos de aprendizaje automático para caracterizar la señal de la tos de pacientes con COVID-19

Christian Salamea-Palacios, Tarquino Sánchez-Almeida, Xavier Calderón-Hinojosa, Javier Guaña-Moya, Paulo Castañeda-Romero, Jessica Reina-Trávez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

Automatic recognition of audio signals is a challenging signal task due to the difficulty of extracting important attributes from such signals, which relies heavily on discriminating acoustic features to determine the type of cough audio coming from COVID-19 patients. In this work, the use of state-of-the-art pre-trained models and a convolutional neural network for the extraction of characteristics of a cough signal from patients with COVID-19 is analyzed. A comparison of three machine learning models has been proposed to extract the features containing relevant information, leading to the recognition of the COVID-19 cough signal. The first model is based on a basic convolutional neural network, the second is based on a YAMNet pre-treatment model, and the third is a VGGish pre-trained model. The experimental results carried out with a ComPare 2021 CCS database show that models, of the three, used, VGGish to provide better performance when extracting the characteristics of the audio signals of the COVID-19 cough signal, having as results the performance metrics f1 score and accuracy with values of 30.76% and 80.51%, representing an improvement of 6.06% and 3.61% compared to the YANMet model, and the confusion matrices, which validate the mentioned model.

Título traducido de la contribuciónMachine learning models to characterize the cough signal of patients with COVID-19
Idioma originalEspañol
Título de la publicación alojada20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtítulo de la publicación alojada"Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable Actions", LACCEI 2022
EditoresMaria M. Larrondo Petrie, Jose Texier, Andrea Pena, Jose Angel Sanchez Viloria
EditorialLatin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)9786289520705
DOI
EstadoPublicada - 2022
Evento20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022 - Boca Raton, Estados Unidos
Duración: 18 jul. 202222 jul. 2022

Serie de la publicación

NombreProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen2022-July
ISSN (versión digital)2414-6390

Conferencia

Conferencia20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022
País/TerritorioEstados Unidos
CiudadBoca Raton
Período18/07/2222/07/22

Nota bibliográfica

Publisher Copyright:
© 2022 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Palabras clave

  • characterization
  • Convolutional Neural Network
  • COVID-19
  • Transfer Learning
  • Vggish
  • YAMNet

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