Music Datasets

Music datasets are crucial for advancing machine learning in music information retrieval (MIR), enabling tasks like genre classification, music recommendation, and lyrics alignment. Current research focuses on developing larger, more diverse datasets encompassing various musical styles and cultures, alongside improved annotation schemes and ontologies for better data interoperability. Researchers are exploring contrastive learning and other deep learning architectures (e.g., CNNs, Transformers) to generate robust audio embeddings and improve model performance on downstream tasks, while also investigating the use of AI-generated music for data augmentation and benchmark creation. These advancements are driving progress in MIR and facilitating the development of more sophisticated music-related applications.