# CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval

arXiv: https://arxiv.org/abs/2304.11029 · HTML: https://mlsanity.com/paper/2304.11029/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2023.

## Built on by (most cited first, 14 of 14)

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [MuPT: A Generative Symbolic Music Pretrained Transformer](https://mlsanity.com/paper/2404.06393/) (2024)
- [NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms](https://mlsanity.com/paper/2502.18008/) (2025)
- [Beyond Language Models: Byte Models are Digital World Simulators](https://mlsanity.com/paper/2402.19155/) (2024)
- [MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models](https://mlsanity.com/paper/2310.11954/) (2023)
- [Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling](https://mlsanity.com/paper/2310.16334/) (2023)
- [Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation](https://mlsanity.com/paper/2407.20445/) (2024)
- [Moonbeam: A MIDI Foundation Model Using Both Absolute and Relative Music Attributes](https://mlsanity.com/paper/2505.15559/) (2025)
- [Composer Vector: Style-steering Symbolic Music Generation in a Latent Space](https://mlsanity.com/paper/2604.03333/) (2026)
- [Prevailing Research Areas for Music AI in the Era of Foundation Models](https://mlsanity.com/paper/2409.09378/) (2024)
- [PBSCR: The Piano Bootleg Score Composer Recognition Dataset](https://mlsanity.com/paper/2401.16803/) (2024)
- [MindMelody: A Closed-Loop EEG-Driven System for Personalized Music Intervention](https://mlsanity.com/paper/2605.01235/) (2026)
- [Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding](https://mlsanity.com/paper/2606.08722/) (2026)
- [AuraLuxMuse: Adaptive Fusion Modeling for Aesthetic Stage Lighting Design with Music and Expert Guidance](https://mlsanity.com/paper/2610.11792/) (2026)

## Abstract (arXiv)

We introduce CLaMP: Contrastive Language-Music Pre-training, which learns cross-modal representations between natural language and symbolic music using a music encoder and a text encoder trained jointly with a contrastive loss. To pre-train CLaMP, we collected a large dataset of 1.4 million music-text pairs. It employed text dropout as a data augmentation technique and bar patching to efficiently represent music data which reduces sequence length to less than 10%. In addition, we developed a masked music model pre-training objective to enhance the music encoder's comprehension of musical context and structure. CLaMP integrates textual information to enable semantic search and zero-shot classification for symbolic music, surpassing the capabilities of previous models. To support the evaluation of semantic search and music classification, we publicly release WikiMusicText (WikiMT), a dataset of 1010 lead sheets in ABC notation, each accompanied by a title, artist, genre, and description. In comparison to state-of-the-art models that require fine-tuning, zero-shot CLaMP demonstrated comparable or superior performance on score-oriented datasets. Our models and code are available at https://github.com/microsoft/muzic/tree/main/clamp.