التفاصيل البيبلوغرافية
| العنوان: |
CLAMP: CONTRASTIVE LANGUAGE-MUSIC PRE-TRAINING FOR CROSS-MODAL SYMBOLIC MUSIC INFORMATION RETRIEVAL. |
| المؤلفون: |
Shangda Wu, Dingyao Yu, Xu Tan, Maosong Sun |
| المصدر: |
International Society for Music Information Retrieval Conference Proceedings; 2023, p157-165, 9p |
| مصطلحات موضوعية: |
MUSIC, INFORMATION retrieval, NATURAL languages, DATA augmentation, COMPREHENSION |
| مستخلص: |
We introduce CLaMP: Contrastive Language-Music Pretraining, 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 finetuning, 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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
