Recent advancements in AI have democratized its deployment as a healthcare assistant.
While pretrained models from large-scale visual and audio datasets have demonstrably …

Listening to lung sounds using a stethoscope is still one of the most important methods to
diagnose respiratory diseases. These sounds are complex and challenging to diagnose, as …

Self-supervised learning (SSL) using masked prediction has made great strides in general-
purpose audio representation. This study proposes Masked Modeling Duo (M2D), an …

Semi-supervised learning effectively mitigates the lack of labeled data by introducing
extensive unlabeled data. Despite achieving success in respiratory sound classification, in …

This paper presents a supervised contrastive learning (SCL) framework for respiratory
sound classification and the hardware implementation of learned ResNet on field …

The high incidence and mortality rates associated with respiratory diseases underscores the
importance of early screening. Machine learning models can automate clinical consultations …

Deep neural networks have been applied to audio spectrograms for respiratory sound
classification. Existing models often treat the spectrogram as a synthetic image while …

Goal: In light of the COVID-19 pandemic, the early diagnosis of respiratory diseases has
become increasingly crucial. Traditional diagnostic methods such as computed tomography …

Respiratory sound classification (RSC) is challenging due to varied acoustic signatures,
primarily influenced by patient demographics and recording environments. To address this …

This study aims to develop an auxiliary diagnostic system for classifying abnormal lung
respiratory sounds, enhancing the accuracy of automatic abnormal breath sound …