2 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles
Miika Toikkanen, June-Woo Kim
Respiratory sound datasets are limited in size and quality, making high performance difficult to achieve. Ensemble models help but inevitably increase compute cost at inference tim…
BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification
June-Woo Kim, Miika Toikkanen, Yera Choi +2
Respiratory sound classification (RSC) is challenging due to varied acoustic signatures, primarily influenced by patient demographics and recording environments. To address this is…
RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification
June-Woo Kim, Miika Toikkanen, Sangmin Bae +2
Recent advancements in AI have democratized its deployment as a healthcare assistant. While pretrained models from large-scale visual and audio datasets have demonstrably generaliz…
Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance
June-Woo Kim, Chihyeon Yoon, Miika Toikkanen +2
Deep generative models have emerged as a promising approach in the medical image domain to address data scarcity. However, their use for sequential data like respiratory sounds is…