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 machine learning-based voice analysis


Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures

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Pulmonary pathologies can be uniquely detectable from the study of the voice signal. Current screening techniques for COVID-19 are limited in both accuracy and frequency in time. Custom Adaboost and CNN architectures are employed and compared for the detection of COVID-19 from smartphone recordings. Acoustic features are identified as voice biomarkers for COVID-19; the RASTA filtering is a noise-robust, effective domain. COVID-positive and recovered subjects can be discriminated from healthy subjects.