10 papers
Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification
Hemansh Shridhar, Miika Toikkanen, June-Woo Kim
Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Transformer (AST). While effective…
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…
A Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction
June-Woo Kim, Kangwook Jang, Minu Kim +1
Ambulatory neck-surface acceleration enables non-invasive monitoring of vocal hyperfunction, yet robust biomarkers for its subtypes remain limited. This study investigates the Neck…
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…
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…
Understanding Frechet Speech Distance for Synthetic Speech Quality Evaluation
June-Woo Kim, Dhruv Agarwal, Federica Cerina
Objective evaluation of synthetic speech quality remains a critical challenge. Human listening tests are the gold standard, but costly and impractical at scale. Fréchet Distance h…