455 citations · 685 across the 20 of their papers we have counts for
9 papers · 1 filter
RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering
Gaia A. Bertolino, Yuwei Zhang, Tong Xia +2
Conversational generative AI is increasingly explored in healthcare, where models must integrate heterogeneous patient signals and support diverse interaction styles while producin…
RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity
Gaia A. Bertolino, Yuwei Zhang, Tong Xia +2
As conversational multimodal AI tools are increasingly adopted to process patient data for health assessment, robust benchmarks are needed to measure progress and expose failure mo…
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…
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Ting Dang, Jing Han, Tong Xia +9
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 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…
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
Chloë Brown, Jagmohan Chauhan, Andreas Grammenos +6
Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or a…