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CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models

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arxiv 2410.13267 v2 pith:6ZEHWPF7 submitted 2024-10-17 cs.SD cs.CLeess.AS

Shangda Wu , Yashan Wang , Ruibin Yuan , Zhancheng Guo , Xu Tan , Ge Zhang , Monan Zhou , Jing Chen
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Xuefeng Mu Yuejie Gao Yuanliang Dong Jiafeng Liu Xiaobing Li Feng Yu Maosong Sun
This is my paper · ORCID
classification cs.SDcs.CLeess.AS
keywords musicclampinformationretrievallanguagemultilingualmultimodalmusical
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection

    cs.SD 2025-07 conditional novelty 6.0 of 10

    A single transformer model aligns scores to performances, transcribes piano audio, and detects mistakes, including faithful handling of repeat sections without pre-unfolded scores.

  2. Scaling Self-Supervised Representation Learning for Symbolic Piano Performance

    cs.SD 2025-06 conditional novelty 6.0 of 10

    Self-supervised pretraining on 60,000 hours of symbolic piano music produces a generative model and contrastive embeddings that beat leading baselines on continuation quality and several MIR classification benchmarks.

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

    cs.SD 2025-02 conditional novelty 5.0 of 10

    A contrastive learning framework (CLaMP 3) aligns three music modalities with multilingual text, enabling text-to-music retrieval, cross-lingual retrieval for unseen languages, and emergent cross-modal retrieval.

  4. PianoBind: A Multimodal Joint Embedding Model for Pop-piano Music

    cs.SD 2025-09 conditional novelty 4.0 of 10

    PianoBind, a trimodal audio-MIDI-text embedding model trained on piano data, beats general-purpose music embedding models on pop-piano text-to-music retrieval benchmarks.

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