Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional
stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and …

Community-Acquired Pneumonia (CAP) is a significant cause of child mortality globally, due
to the lack of ubiquitous monitoring methods. Clinically, the wireless stethoscope can be a …

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 medical …

Abstract Machine learning methods for classifying respiratory anomalies have provided low
classification accuracy. So high classification accuracy is required in order to utilize …

This paper presents a deep learning system applied for detecting anomalies from respiratory
sound recordings. Our system initially performs audio feature extraction using Continuous …

This paper presents a deep learning system applied for detecting anomalies from respiratory
sound recordings. Initially, our system begins with audio feature extraction using …

Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive
respiratory monitoring, has demonstrated considerable promise in the early detection of …

Real-world sound signals exhibit various aspects of grouping and profiling behaviors, such
as being recorded from identical sources, having similar environmental settings, or …

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

Automatic voice pathology detection is promising for noninvasive screening and early
intervention using sound signals. Nevertheless, existing methods are susceptible to …