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

Deep learning approaches are gaining popularity in the medical field for diagnostics and
predictive analytics. This study aims to improve the classification of respiratory sounds …

Acoustic anomaly detection aims at distinguishing abnormal acoustic signals from the
normal ones. It suffers from the class imbalance issue and the lacking in the abnormal …

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 …

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

Lung sounds contain important clinical information which can be used for identifying
respiratory and/or lung disorders. Manual identification of respiratory events is time …

This study presents a novel methodology utilizing a pre-trained speech recognition model
for processing respiratory sound data. By incorporating medical record information, we …

Objective. Auscultation of lung sound plays an important role in the early diagnosis of lung
diseases. This work aims to develop an automated adventitious lung sound detection …

Lung Auscultation is a non-invasive process of distinguishing normal respiratory sounds
from abnormal ones by analyzing the airflow along the respiratory tract. With developments …

Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of
machines from normal sounds. However, the state-of-the-art approaches are not always …