Song Dataset

Song datasets are crucial for advancing music information retrieval (MIR) and artificial intelligence (AI) applications in music. Current research focuses on developing and improving datasets for various tasks, including music captioning (using models like FUTGA), era recognition (leveraging supervised contrastive learning), and description-to-song generation (employing novel frameworks like MuDiT/MuSiT). These efforts aim to create more comprehensive and nuanced datasets that address limitations in existing resources, such as data scarcity, bias, and the lack of fine-grained annotations, ultimately improving the accuracy and capabilities of AI models for music analysis and generation.