7 citations · 7 across the 11 of their papers we have counts for
4 papers · 1 filter
Quality Adaptive Angular Margin Learning for Respiratory Sound Classification
Yoon Tae Kim, Heejoon Koo, Miika Toikkanen +1
We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-class separability. Our framewor…
Mitigating Stethoscope-Induced Shortcuts in Respiratory Sound Classification under Federated Domain Generalization with Causality-Inspired Interventions
Heejoon Koo, Yoon Tae Kim, Miika Toikkanen +1
AI-driven respiratory sound classification (RSC) is promising for automated pulmonary disease detection, yet multi-site deployment is hindered by inter-stethoscope variability. We…
Can Large Audio Language Models Ignore Multilingual Distractors? An Evaluation of Their Selective Auditory Attention Capabilities
Heejoon Koo
Robust selective auditory attention under multilingual interference is critical for reliable LALM deployment. We introduce MUSI, a cocktail party-inspired controlled diagnostic eva…
Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification
June-Woo Kim, Miika Toikkanen, Heejoon Koo +3
Training reliable respiratory sound classification models remains challenging due to the limited size and subject diversity of datasets. Ensemble methods can improve robustness, bu…