Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908628547534848 |
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| author | Ou, Longshen Zhao, Jingwei Wang, Ziyu Xia, Gus Liang, Qihao Wang, Torin Hopkins Ye |
| author_facet | Ou, Longshen Zhao, Jingwei Wang, Ziyu Xia, Gus Liang, Qihao Wang, Torin Hopkins Ye |
| contents | We present a unified framework for automatic multitrack music arrangement that enables a single pre-trained symbolic music model to handle diverse arrangement scenarios, including reinterpretation, simplification, and additive generation. At its core is a segment-level reconstruction objective operating on token-level disentangled content and style, allowing for flexible any-to-any instrumentation transformations at inference time. To support track-wise modeling, we introduce REMI-z, a structured tokenization scheme for multitrack symbolic music that enhances modeling efficiency and effectiveness for both arrangement tasks and unconditional generation. Our method outperforms task-specific state-of-the-art models on representative tasks in different arrangement scenarios -- band arrangement, piano reduction, and drum arrangement, in both objective metrics and perceptual evaluations. Taken together, our framework demonstrates strong generality and suggests broader applicability in symbolic music-to-music transformation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization Ou, Longshen Zhao, Jingwei Wang, Ziyu Xia, Gus Liang, Qihao Wang, Torin Hopkins Ye Sound Computation and Language Audio and Speech Processing We present a unified framework for automatic multitrack music arrangement that enables a single pre-trained symbolic music model to handle diverse arrangement scenarios, including reinterpretation, simplification, and additive generation. At its core is a segment-level reconstruction objective operating on token-level disentangled content and style, allowing for flexible any-to-any instrumentation transformations at inference time. To support track-wise modeling, we introduce REMI-z, a structured tokenization scheme for multitrack symbolic music that enhances modeling efficiency and effectiveness for both arrangement tasks and unconditional generation. Our method outperforms task-specific state-of-the-art models on representative tasks in different arrangement scenarios -- band arrangement, piano reduction, and drum arrangement, in both objective metrics and perceptual evaluations. Taken together, our framework demonstrates strong generality and suggests broader applicability in symbolic music-to-music transformation. |
| title | Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2408.15176 |