Interpreting Graphic Notation with MusicLDM: An AI Improvisation of Cornelius Cardew's Treatise
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912153537085440 |
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| author | Karchkhadze, Tornike Shao, Keren Dubnov, Shlomo |
| author_facet | Karchkhadze, Tornike Shao, Keren Dubnov, Shlomo |
| contents | This work presents a novel method for composing and improvising music inspired by Cornelius Cardew's Treatise, using AI to bridge graphic notation and musical expression. By leveraging OpenAI's ChatGPT to interpret the abstract visual elements of Treatise, we convert these graphical images into descriptive textual prompts. These prompts are then input into MusicLDM, a pre-trained latent diffusion model designed for music generation. We introduce a technique called "outpainting," which overlaps sections of AI-generated music to create a seamless and cohesive composition. We demostrate a new perspective on performing and interpreting graphic scores, showing how AI can transform visual stimuli into sound and expand the creative possibilities in contemporary/experimental music composition. Musical pieces are available at https://bit.ly/TreatiseAI |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08944 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interpreting Graphic Notation with MusicLDM: An AI Improvisation of Cornelius Cardew's Treatise Karchkhadze, Tornike Shao, Keren Dubnov, Shlomo Sound Machine Learning Audio and Speech Processing This work presents a novel method for composing and improvising music inspired by Cornelius Cardew's Treatise, using AI to bridge graphic notation and musical expression. By leveraging OpenAI's ChatGPT to interpret the abstract visual elements of Treatise, we convert these graphical images into descriptive textual prompts. These prompts are then input into MusicLDM, a pre-trained latent diffusion model designed for music generation. We introduce a technique called "outpainting," which overlaps sections of AI-generated music to create a seamless and cohesive composition. We demostrate a new perspective on performing and interpreting graphic scores, showing how AI can transform visual stimuli into sound and expand the creative possibilities in contemporary/experimental music composition. Musical pieces are available at https://bit.ly/TreatiseAI |
| title | Interpreting Graphic Notation with MusicLDM: An AI Improvisation of Cornelius Cardew's Treatise |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.08944 |