Music Retrieval

Music retrieval research focuses on efficiently finding music based on various inputs, including text descriptions, video content, or even other music. Current efforts concentrate on developing robust cross-modal models, often employing transformer architectures and contrastive learning, to effectively bridge the gap between different data types (e.g., audio, video, text, sheet music). These advancements are improving music recommendation systems, enabling more intuitive content discovery, and facilitating creative applications like video-music synchronization and music generation. The field's impact extends to enhancing user experience in music platforms and fostering new research directions in multimodal learning and AI-driven music creation.