Computer Science > Sound
arXiv:2404.18081 (cs)
[Submitted on 28 Apr 2024 (v1), last revised 30 Apr 2024 (this version, v2)]
Title:ComposerX: Multi-Agent Symbolic Music Composition with LLMs
Authors:Qixin Deng,Qikai Yang,Ruibin Yuan,Yipeng Huang,Yi Wang,Xubo Liu,Zeyue Tian,Jiahao Pan,Ge Zhang,Hanfeng Lin,Yizhi Li,Yinghao Ma,Jie Fu,Chenghua Lin,Emmanouil Benetos,Wenwu Wang,Guangyu Xia,Wei Xue,Yike Guo
View a PDF of the paper titled ComposerX: Multi-Agent Symbolic Music Composition with LLMs, by Qixin Deng and 18 other authors
View PDFAbstract:Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints. While demonstrating impressive capabilities in STEM subjects, current LLMs easily fail in this task, generating ill-written music even when equipped with modern techniques like In-Context-Learning and Chain-of-Thoughts. To further explore and enhance LLMs' potential in music composition by leveraging their reasoning ability and the large knowledge base in music history and theory, we propose ComposerX, an agent-based symbolic music generation framework. We find that applying a multi-agent approach significantly improves the music composition quality of GPT-4. The results demonstrate that ComposerX is capable of producing coherent polyphonic music compositions with captivating melodies, while adhering to user instructions.
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2404.18081 [cs.SD] |
| (orarXiv:2404.18081v2 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2404.18081 arXiv-issued DOI via DataCite |
Submission history
From: Qikai Yang [view email][v1] Sun, 28 Apr 2024 06:17:42 UTC (8,874 KB)
[v2] Tue, 30 Apr 2024 14:14:26 UTC (8,874 KB)
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View a PDF of the paper titled ComposerX: Multi-Agent Symbolic Music Composition with LLMs, by Qixin Deng and 18 other authors
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