Apinan Hasthanasombat — accepted AI-conference papers

2021

COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening

Tong Xia, Dimitris Spathis, Chlo{\"e} Brown, J Ch, Andreas Grammenos, Jing Han +6

NeurIPS 2021poster

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 through machine learning. However, there has also been scepticism regarding the initial efforts, due to perhaps the lack o…

Cited by 84 Source ↗Scholar ↗

2021

Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data

Jing Han, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis +3

ICASSP 2021accepted

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, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we p…

Cited by 0 Source ↗Scholar ↗