324 citations · 625 across the 27 of their papers we have counts for
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eess.AS2026
Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening
Wensi Zhang, Tomas Teijeiro, Jérôme Thevenot +1
Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics or artifacts of data collecti…
eess.AS2024★ 7 cited
Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge
Stefano Albini, Lara Orlandic, Jonathan Dan +4
Continuous cough monitors can greatly aid doctors in home monitoring and treatment of respiratory diseases. Although many algorithms have been proposed, they still face limitations…