64 citations · 75 across the 7 of their papers we have counts for
8 papers
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…
Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification
June-Woo Kim, Sangmin Bae, Won-Yang Cho +2
Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available da…
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…
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification
Sangmin Bae, June-Woo Kim, Won-Yang Cho +7
Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medica…
Multilingual Open Text Release 1: Public Domain News in 44 Languages
Chester Palen-Michel, June Kim, Constantine Lignos
We present Multilingual Open Text (MOT), a new multilingual corpus containing text in 44 languages, many of which have limited existing text resources for natural language processi…
Vocoder-free End-to-End Voice Conversion with Transformer Network
June-Woo Kim, Ho-Young Jung, Minho Lee
Mel-frequency filter bank (MFB) based approaches have the advantage of learning speech compared to raw spectrum since MFB has less feature size. However, speech generator with MFB…
End-to-end Multimodal Emotion and Gender Recognition with Dynamic Joint Loss Weights
Myungsu Chae, Tae-Ho Kim, Young Hoon Shin +2
Multi-task learning is a method for improving the generalizability of multiple tasks. In order to perform multiple classification tasks with one neural network model, the losses of…