3 papers
cs.LG2026
SECURE-AI: Multi-Signal Uncertainty Quantification for Trustworthy Acoustic Cough Detection
Sejal Solkhan, Feiya Xiang, Mayur Sanap +2
Acoustic models for cough detection achieve high accuracy on curated benchmarks but fail silently in deployment, where speech, environmental noise, and audio unlike the training da…
eess.AS2026
BCoughBench: Benchmarking Respiratory Acoustic Foundation Models Under Body-Coupled Wearable Sensor Conditions
Mayur Sanap, Prasanna Desikan, Edgar Lobaton
Respiratory acoustic foundation models (FMs) are benchmarked exclusively on smartphone recordings, yet clinical deployment increasingly targets body-coupled (BC) wearables whose se…
cs.LG2026
Beyond Classification: A Cough Regression Benchmark for Respiratory Acoustic Foundation Models
Mayur Sanap, Prasanna Desikan, Edgar Lobaton
Respiratory acoustic foundation models (FMs) excel at cough classification, yet their ability to predict continuous health quantities from cough audio remains largely unexplored, d…