Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

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The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpa ...

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

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DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries