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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.25498 |
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| _version_ | 1866914513890050048 |
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| author | He, Xuzheng Nan, Nan Wang, Zhilin Kang, Ziyue Mo, Zhuoru Li, Ao Pan, Yu Li, Xiaobing Yu, Feng Guan, Xiaohong |
| author_facet | He, Xuzheng Nan, Nan Wang, Zhilin Kang, Ziyue Mo, Zhuoru Li, Ao Pan, Yu Li, Xiaobing Yu, Feng Guan, Xiaohong |
| contents | Generating symphonic music requires simultaneously managing high-level structural form and dense, multi-track orchestration. Existing symbolic models often struggle with a "complexity-control imbalance", in which scaling bottlenecks limit long-term granular steerability. We present SymphonyGen, a 3D hierarchical framework for contemporary cinematic orchestration. SymphonyGen employs a cascading decoder architecture that decomposes the Bar, Track, and Event axes, improving computational efficiency and scalability over conventional 1D or 2D models. We introduce "short-score" conditioning via a beat-quantized multi-voice harmony skeleton, enabling outline control while preserving textural diversity. The model is further refined using Group Relative Policy Optimization (GRPO) with a cross-modal audio-perceptual reward, aligning symbolic output with modern acoustic expectations. Additionally, we implement a dissonance-averse sampling algorithm to suppress unintended tonal clashes during inference. Objective evaluations show that both reinforcement learning and dissonance-averse sampling effectively enhance harmonic cleanliness while maintaining melodic expression. Subjective evaluations demonstrate that SymphonyGen outperforms baselines in musicality and preference for orchestral music generation. Demo page: https://symphonygen.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25498 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton He, Xuzheng Nan, Nan Wang, Zhilin Kang, Ziyue Mo, Zhuoru Li, Ao Pan, Yu Li, Xiaobing Yu, Feng Guan, Xiaohong Sound Artificial Intelligence Generating symphonic music requires simultaneously managing high-level structural form and dense, multi-track orchestration. Existing symbolic models often struggle with a "complexity-control imbalance", in which scaling bottlenecks limit long-term granular steerability. We present SymphonyGen, a 3D hierarchical framework for contemporary cinematic orchestration. SymphonyGen employs a cascading decoder architecture that decomposes the Bar, Track, and Event axes, improving computational efficiency and scalability over conventional 1D or 2D models. We introduce "short-score" conditioning via a beat-quantized multi-voice harmony skeleton, enabling outline control while preserving textural diversity. The model is further refined using Group Relative Policy Optimization (GRPO) with a cross-modal audio-perceptual reward, aligning symbolic output with modern acoustic expectations. Additionally, we implement a dissonance-averse sampling algorithm to suppress unintended tonal clashes during inference. Objective evaluations show that both reinforcement learning and dissonance-averse sampling effectively enhance harmonic cleanliness while maintaining melodic expression. Subjective evaluations demonstrate that SymphonyGen outperforms baselines in musicality and preference for orchestral music generation. Demo page: https://symphonygen.github.io/ |
| title | SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2604.25498 |