Health Acoustic

Health acoustics leverages audio signals like coughs and breaths to diagnose and monitor health conditions, offering a potentially non-invasive alternative to traditional methods. Current research heavily employs deep learning, particularly self-supervised learning with architectures like masked autoencoders and contrastive learning models, to extract meaningful features from these complex audio datasets. However, challenges remain in addressing the multi-source nature of audio signals and ensuring the robustness and generalizability of these models, highlighting the need for rigorous validation and data augmentation techniques. Overcoming these limitations could significantly impact healthcare by enabling more accessible, continuous, and objective health monitoring.