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

Understand

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.