Pay Attention to the Speech: COVID-19 Diagnosis using Machine Learning and Crowdsourced Respiratory and Speech Recordings
TLDR
In this paper, the authors used the Coswara dataset where each user has recorded 9 different types of sounds as cough, breathing, and speech labeled with COVID-19 status and showed that using simple binary classifiers can achieve an AUC of 96.4% and an accuracy of 96% by averaging the predictions of multiple models trained separately on different sound types.Abstract:
Since the outbreak of COVID-19, many efforts have been made to utilize the respiratory sounds and coughs collected by smartphones for training Machine Learning models to classify and distinguish COVID-19 sounds from healthy ones. Embedding those models into mobile applications or Internet of things devices can make effective COVID-19 pre-screening tools afforded by anyone anywhere. Most of the previous researchers trained their classifiers with respiratory sounds such as breathing or coughs, and they achieved promising results. We claim that using special voice patterns besides other respiratory sounds can achieve better performance. In this study, we used the Coswara dataset where each user has recorded 9 different types of sounds as cough, breathing, and speech labeled with COVID-19 status. A combination of models trained on different sounds can diagnose COVID-19 more accurately than a single model trained on cough or breathing only. Our results show that using simple binary classifiers can achieve an AUC of 96.4% and an accuracy of 96% by averaging the predictions of multiple models trained and evaluated separately on different sound types. Finally, this study aims to draw attention to the importance of the human voice alongside other respiratory sounds for the sound-based COVID-19 diagnosis.read more
Citations
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Journal ArticleDOI
Diagnosing COVID-19 using artificial intelligence: a comprehensive review
Varada Vivek Khanna,Krishnaraj Chadaga,Niranjana Sampathila,Srikanth Prabhu,Rajagopal Chadaga,Shashikiran Umakanth +5 more
TL;DR: A comprehensive review of the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth as discussed by the authors .
Journal ArticleDOI
Diagnosing COVID-19 using artificial intelligence: a comprehensive review
Varada Vivek Khanna,Krishnaraj Chadaga,Niranjana Sampathila,Srikanth Prabhu,Rajagopal Chadaga,Shashikiran Umakanth +5 more
TL;DR: A comprehensive review of the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth as discussed by the authors .
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Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks
TL;DR: In this article , a novel approach is proposed to pre-process the data and pass it through a newly proposed CNN architecture, which helps to make an accurate diagnosis of lung sounds.
Journal ArticleDOI
The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests
TL;DR: In this article , an interval temporal logic decision tree extraction algorithm was proposed to improve the performance of symbolic learning for automated classification of COVID-19-positive cough and breath recordings.
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COVID-19 activity screening by a smart-data-driven multi-band voice analysis
TL;DR: In this article , three different signal analyses have been applied (per broadband, per sub-bands and per broadband & subbands) to Cough, Breathing and Speech signals of Coswara dataset to extract nonlinear patterns (Energy, Entropies, Correlation Dimension, Detrended Fluctuation Analysis, Lyapunov Exponent & Fractal Dimensions) for feeding a XGBoost classifier to discriminate COVID-19 activity on its different stages.
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