Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866916375063166976 |
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| author | Zhang, Yixiao Maezawa, Akira Xia, Gus Yamamoto, Kazuhiko Dixon, Simon |
| author_facet | Zhang, Yixiao Maezawa, Akira Xia, Gus Yamamoto, Kazuhiko Dixon, Simon |
| contents | Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface. The system uses a large language model to interpret user intentions and select appropriate AI models for task execution. Each backend model is specialized for a specific task, and their outputs are aggregated to meet the user's requirements. To ensure musical coherence, essential attributes are maintained in a centralized table. We evaluate the effectiveness of the proposed system through semi-structured interviews and questionnaires, highlighting its utility not only in facilitating music creation but also its potential for broader applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_12404 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing Zhang, Yixiao Maezawa, Akira Xia, Gus Yamamoto, Kazuhiko Dixon, Simon Sound Computation and Language Human-Computer Interaction Machine Learning Audio and Speech Processing Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface. The system uses a large language model to interpret user intentions and select appropriate AI models for task execution. Each backend model is specialized for a specific task, and their outputs are aggregated to meet the user's requirements. To ensure musical coherence, essential attributes are maintained in a centralized table. We evaluate the effectiveness of the proposed system through semi-structured interviews and questionnaires, highlighting its utility not only in facilitating music creation but also its potential for broader applications. |
| title | Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing |
| topic | Sound Computation and Language Human-Computer Interaction Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2310.12404 |