Respiratory Sound

Respiratory sound analysis aims to automatically detect and classify respiratory diseases using audio recordings of coughs, breaths, and other sounds, improving diagnostic accuracy and accessibility. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs), vision transformers, and recurrent neural networks (RNNs), often incorporating techniques like contrastive learning, multi-task learning, and data augmentation to address challenges such as class imbalance and data scarcity. These advancements hold significant promise for improving early disease detection, particularly in resource-limited settings, and facilitating remote patient monitoring through the use of readily available devices like smartphones and digital stethoscopes.