CoughPhase-CLR uses cough physiological phases to build contrastive positive pairs, outperforming random cropping on downstream tasks including COVID-19 detection and COPD classification.
Exploring automatic diagnosis of covid-19 from crowdsourced respiratory sound data
3 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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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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CoughPhase-CLR: Designing an acoustics-informed foundation model for coughing sound classification
CoughPhase-CLR uses cough physiological phases to build contrastive positive pairs, outperforming random cropping on downstream tasks including COVID-19 detection and COPD classification.
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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.