CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages

CLaMP 3 is a unified framework developed to address challenges of cross-modal and crosslingual generalization in music information retrieval.Using contrastive learning, it aligns all major music modalities-including sheet music, performance signals, and audio recordingswith multilingual text in a shared representation space, enabling retrieval across unaligned modalities with text as a bridge.It features a multilingual text encoder adaptable to unseen languages, exhibiting strong cross-lingual generalization.Leveraging retrieval-augmented generation, we curated M4-RAG, a web-scale dataset consisting of 2.31 million music-text pairs.This dataset is enriched with detailed metadata that represents a wide array of global musical traditions.To advance future research, we release WikiMT-X, a benchmark comprising 1,000 triplets of sheet music, audio, and richly varied text descriptions.Experiments show that CLaMP 3 achieves state-of-the-art performance on multiple MIR tasks, significantly surpassing previous strong baselines and demonstrating excellent generalization in multimodal and multilingual music contexts.