COVID-19 heavily affects breathing and voice and causes symptoms that make patients'
voices distinctive, creating recognizable audio signatures. Initial studies have already …

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 …

The COVID-19 pandemic created significant interest and demand for infection detection and
monitoring solutions. In this paper, we propose a machine learning method to quickly detect …

The COVID-19 pandemic presents global challenges transcending boundaries of country,
race, religion, and economy. The current gold standard method for COVID-19 detection is …

Goal: We hypothesized that COVID-19 subjects, especially including asymptomatics, could
be accurately discriminated only from a forced-cough cell phone recording using Artificial …

Audio signals generated by the human body (eg, sighs, breathing, heart, digestion, vibration
sounds) have routinely been used by clinicians as indicators to diagnose disease or assess …

The COVID-19 outbreak was announced as a global pandemic by the World Health
Organization in March 2020 and has affected a growing number of people in the past few …

The development of fast and accurate screening tools, which could facilitate testing and
prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context …

Audio signals are widely recognised as powerful indicators of overall health status, and
there has been increasing interest in leveraging sound for affordable COVID-19 screening …

We present an experimental investigation into the effectiveness of transfer learning and
bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath …