5 citations · 8 across the 5 of their papers we have counts for
7 papers · 1 filter
Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking
Yuwei Zhang, Tong Xia, Jing Han +6
Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for th…
A Summary of the ComParE COVID-19 Challenges
Harry Coppock, Alican Akman, Christian Bergler +17
The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and c…
Segmentation-free Heart Pathology Detection Using Deep Learning
Erika Bondareva, Jing Han, William Bradlow +1
Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination. The ability to diagnose a…
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
Jing Han, Tong Xia, Dimitris Spathis +9
Researchers have been battling with the question of how we can identify Coronavirus disease (COVID-19) cases efficiently, affordably and at scale. Recent work has shown how audio b…
Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
Tong Xia, Jing Han, Lorena Qendro +2
Recently, sound-based COVID-19 detection studies have shown great promise to achieve scalable and prompt digital pre-screening. However, there are still two unsolved issues hinderi…
Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
Jing Han, Chloë Brown, Jagmohan Chauhan +6
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 con…