@inproceedings{wu-etal-2025-clamp,
    title = "{CL}a{MP} 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models",
    author = "Wu, Shangda  and
      Wang, Yashan  and
      Yuan, Ruibin  and
      Zhancheng, Guo  and
      Tan, Xu  and
      Zhang, Ge  and
      Zhou, Monan  and
      Chen, Jing  and
      Mu, Xuefeng  and
      Gao, Yuejie  and
      Dong, Yuanliang  and
      Liu, Jiafeng  and
      Li, Xiaobing  and
      Yu, Feng  and
      Sun, Maosong",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.27/",
    pages = "435--451",
    ISBN = "979-8-89176-195-7",
    abstract = "Challenges in managing linguistic diversity and integrating various musical modalities are faced by current music information retrieval systems. These limitations reduce their effectiveness in a global, multimodal music environment. To address these issues, we introduce CLaMP 2, a system compatible with 101 languages that supports both ABC notation (a text-based musical notation format) and MIDI (Musical Instrument Digital Interface) for music information retrieval. CLaMP 2, pre-trained on 1.5 million ABC-MIDI-text triplets, includes a multilingual text encoder and a multimodal music encoder aligned via contrastive learning. By leveraging large language models, we obtain refined and consistent multilingual descriptions at scale, significantly reducing textual noise and balancing language distribution. Our experiments show that CLaMP 2 achieves state-of-the-art results in both multilingual semantic search and music classification across modalities, thus establishing a new standard for inclusive and global music information retrieval."
}