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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2404.18081 |
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| _version_ | 1866916229370871808 |
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| author | Deng, Qixin Yang, Qikai Yuan, Ruibin Huang, Yipeng Wang, Yi Liu, Xubo Tian, Zeyue Pan, Jiahao Zhang, Ge Lin, Hanfeng Li, Yizhi Ma, Yinghao Fu, Jie Lin, Chenghua Benetos, Emmanouil Wang, Wenwu Xia, Guangyu Xue, Wei Guo, Yike |
| author_facet | Deng, Qixin Yang, Qikai Yuan, Ruibin Huang, Yipeng Wang, Yi Liu, Xubo Tian, Zeyue Pan, Jiahao Zhang, Ge Lin, Hanfeng Li, Yizhi Ma, Yinghao Fu, Jie Lin, Chenghua Benetos, Emmanouil Wang, Wenwu Xia, Guangyu Xue, Wei Guo, Yike |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18081 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ComposerX: Multi-Agent Symbolic Music Composition with LLMs Deng, Qixin Yang, Qikai Yuan, Ruibin Huang, Yipeng Wang, Yi Liu, Xubo Tian, Zeyue Pan, Jiahao Zhang, Ge Lin, Hanfeng Li, Yizhi Ma, Yinghao Fu, Jie Lin, Chenghua Benetos, Emmanouil Wang, Wenwu Xia, Guangyu Xue, Wei Guo, Yike Sound Artificial Intelligence Computation and Language Machine Learning Multimedia Audio and Speech Processing 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. |
| title | ComposerX: Multi-Agent Symbolic Music Composition with LLMs |
| topic | Sound Artificial Intelligence Computation and Language Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2404.18081 |