AI 中文总结
该研究通过在线采样器-渲染器流水线，探究合成AMT数据的音符事件分布损坏程度与音色覆盖对模型迁移性能的影响，发现适度损坏音符分布可提升性能，更广泛音色覆盖能增强泛化，为合成AMT数据构建提供了方向。
AI 中文摘要
自动音乐转录（AMT）受限于带有精确符号标注的音频记录稀缺。合成数据可提供大规模监督，但有效迁移是否依赖真实乐谱结构或广泛音色覆盖仍不明确。我们通过在线采样器-渲染器流水线分别研究这些因素：统一的损坏采样器范围从未修改的MIDI片段，经部分损坏到深度随机化的音符事件分布；渲染器将这些事件转换为音频，同时独立控制乐器与音色覆盖。固定转录模型在离线记录与新渲染示例上联合训练。受控消融实验揭示两个因素间的不对称性：音符事件分布的适度损坏不会损害迁移，反而可提升性能；而在固定音符事件分布下，渲染器侧更广泛的音色支持始终能提升域外泛化能力。最后，在线渲染示例在强组合数据 regime 中补充真实数据与现有合成数据。这些结果表明，合成AMT数据应优先覆盖音符级属性及其音色实现，而非真实的联合乐谱结构。
英文摘要
Automatic music transcription (AMT) is limited by the scarcity of audio recordings paired with precise symbolic annotations. Synthetic data can provide supervision at scale, but it remains unclear whether effective transfer depends on realistic score structure or broad timbral coverage. We study these factors separately through an online sampler--renderer pipeline. A unified corruption sampler ranges from unmodified MIDI clips through partial corruption to deeply randomized note-event distributions. The renderer converts these events to audio while independently controlling instrument and timbral coverage. A fixed transcription model is trained jointly on offline recordings and newly rendered examples. Controlled ablations reveal an asymmetry between the two factors: moderate corruption of the note-event distribution does not impair transfer and can improve it, whereas broader renderer-side timbral support consistently improves out-of-domain generalization under a fixed note-event distribution. Finally, online-rendered examples complement real and existing synthetic data in a strong combined-data regime. These results suggest that synthetic AMT data should prioritize coverage of note-level attributes and their timbral realizations over realistic joint score structure.
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