Web: http://arxiv.org/abs/2201.01232

June 23, 2022, 1:11 a.m. | Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat

cs.LG updates on arXiv.org arxiv.org

Recent work has shown the potential of using audio data (eg, cough,
breathing, and voice) in the screening for COVID-19. However, these approaches
only focus on one-off detection and detect the infection given the current
audio sample, but do not monitor disease progression in COVID-19. Limited
exploration has been put forward to continuously monitor COVID-19 progression,
especially recovery, through longitudinal audio data. Tracking disease
progression characteristics could lead to more timely treatment.


The primary objective of this study is to …

arxiv covid covid-19 data deep deep learning development learning model model development prediction validation voice

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