CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
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.
Cite
@article{arxiv.2410.13267,
title = {CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models},
author = {Shangda Wu and Yashan Wang and Ruibin Yuan and Zhancheng Guo and Xu Tan and Ge Zhang and Monan Zhou and Jing Chen and Xuefeng Mu and Yuejie Gao and Yuanliang Dong and Jiafeng Liu and Xiaobing Li and Feng Yu and Maosong Sun},
journal= {arXiv preprint arXiv:2410.13267},
year = {2025}
}
Comments
17 pages, 10 figures, 4 tables, accepted by NAACL 2025