@inproceedings{wu-etal-2025-clamp-3,
    title = "{CL}a{MP} 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages",
    author = "Wu, Shangda  and
      Zhancheng, Guo  and
      Yuan, Ruibin  and
      Jiang, Junyan  and
      Doh, SeungHeon  and
      Xia, Gus  and
      Nam, Juhan  and
      Li, Xiaobing  and
      Yu, Feng  and
      Sun, Maosong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.133/",
    pages = "2605--2625",
    ISBN = "979-8-89176-256-5",
    abstract = "CLaMP 3 is a unified framework developed to address challenges of cross-modal and cross-lingual generalization in music information retrieval. Using contrastive learning, it aligns all major music modalities{--}including sheet music, performance signals, and audio recordings{--}with 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."
}