ConvNet with MFCC features and data augmentation classifies cough sounds for COVID-19 with 87.07 AUC on the DiCOVA 2021 blind test, outperforming the baseline by 23%.
Coswara–a database of breath- ing, cough, and voice sounds for covid-19 diagnosis
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Empirical tuning of MFCC parameters (roughly 30 coefficients, shorter hops, dataset-dependent frame lengths) improves SVM accuracy for respiratory disease detection by 14.9-19.6% on COVID-19 and voice-disorder datasets.
HuBERT reaches 86% accuracy and 0.93 AUC detecting COVID-19 from 893 voice samples in the Cambridge COVID-19 Sound database.
citing papers explorer
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COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation
ConvNet with MFCC features and data augmentation classifies cough sounds for COVID-19 with 87.07 AUC on the DiCOVA 2021 blind test, outperforming the baseline by 23%.
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Optimising MFCC parameters for the automatic detection of respiratory diseases
Empirical tuning of MFCC parameters (roughly 30 coefficients, shorter hops, dataset-dependent frame lengths) improves SVM accuracy for respiratory disease detection by 14.9-19.6% on COVID-19 and voice-disorder datasets.
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Developing a Multi-variate Prediction Model For COVID-19 From Crowd-sourced Respiratory Voice Data
HuBERT reaches 86% accuracy and 0.93 AUC detecting COVID-19 from 893 voice samples in the Cambridge COVID-19 Sound database.