Audio Supervised Learning

Audio supervised learning, encompassing both supervised and self-supervised approaches, aims to build robust audio representations for various downstream tasks by leveraging labeled and unlabeled audio data. Current research heavily emphasizes self-supervised pre-training using transformer-based architectures, often incorporating techniques like masked prediction and teacher-student training to improve efficiency and representation quality. These advancements are driving improvements in audio classification, retrieval, and other applications, particularly through the development of more efficient and effective pre-training methods that can handle large-scale, heterogeneous datasets. Federated learning is also emerging as a key area of focus, enabling collaborative training while preserving data privacy.