@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://aclanthology.org/2025.findings-naacl.27/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
%A Wu, Shangda
%A Wang, Yashan
%A Yuan, Ruibin
%A Zhancheng, Guo
%A Tan, Xu
%A Zhang, Ge
%A Zhou, Monan
%A Chen, Jing
%A Mu, Xuefeng
%A Gao, Yuejie
%A Dong, Yuanliang
%A Liu, Jiafeng
%A Li, Xiaobing
%A Yu, Feng
%A Sun, Maosong
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F wu-etal-2025-clamp
%X 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.
%R 10.18653/v1/2025.findings-naacl.27
%U https://aclanthology.org/2025.findings-naacl.27/
%U https://doi.org/10.18653/v1/2025.findings-naacl.27
%P 435-451
Markdown (Informal)
[CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models](https://aclanthology.org/2025.findings-naacl.27/) (Wu et al., Findings 2025)
ACL
- Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, and Maosong Sun. 2025. CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 435–451, Albuquerque, New Mexico. Association for Computational Linguistics.