Audio Analysis

Audio analysis focuses on extracting meaningful information from sound recordings, aiming to automate tasks like music transcription, genre classification, and medical diagnosis. Current research heavily utilizes deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often combined with techniques like self-supervised learning and contrastive learning to improve model generalization and performance on diverse audio datasets. This field is impactful across various domains, from enhancing music information retrieval and personalized music recommendations to improving healthcare through automated detection of respiratory issues and other health indicators based on vocalizations.