Deep Learning Audio Spectrograms Processing to the Early COVID-19 Detection

Ciro Rodriguez Rodriguez, Daniel Angeles, Renzo Chafloque, Freddy Kaseng, Bishwajeet Pandey

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The objective of the paper is to provide a model capable of serving as a basis for retraining a convolutional neural network that can be used to detect COVID-19 cases through spectrograms of coughing, sneezing and other respiratory sounds from infected people. To address this challenge, the methodology was focused on Deep Learning technics worked with a dataset of sounds of sick and non-sick people, and using ImageNet's Xception architecture to train the model to be presented through Fine-Tuning. The results obtained were a precision of 0.75 to 0.80, this being drastically affected by the quality of the dataset at our availability, however, when getting relatively high results for the conditions of the data used, we can conclude that the model can present much better results if it is working with a dataset specifically of respiratory sounds of COVID-19 cases with high quality.

Original languageEnglish
Title of host publicationProceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020
EditorsGeetam Tomar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages429-434
Number of pages6
ISBN (Electronic)9781728193939
DOIs
StatePublished - 25 Sep 2020
Event12th International Conference on Computational Intelligence and Communication Networks, CICN 2020 - Bhimtal, India
Duration: 25 Sep 202026 Sep 2020

Publication series

NameProceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020

Conference

Conference12th International Conference on Computational Intelligence and Communication Networks, CICN 2020
Country/TerritoryIndia
CityBhimtal
Period25/09/2026/09/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • COVID-19
  • ImageNet's Xception architecture
  • audio spectrograms
  • convolutional neural networks
  • deep learning

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