用分布切换统一三种自动钢琴作曲法，突破人类演奏极限。
Amanous: Distribution-Switching for Superhuman Piano Density on Disklavier
- 通过符号选择不同分布模式，实现风格化高密度纹理生成
- 发现24-30音/秒为计算饱和点，超限后需跨域耦合
- 适合作曲家与交互媒体开发者参考，尤其关注超密度演奏
自动化钢琴可实现远超人类物理极限的音符密度、和声复杂度与音区变换。然而，当前主流的三种作曲方法——Nancarrow的节奏赋格、Xenakis的随机分布、以及L系统文法——长期独立发展。本文提出Amanous，一种面向Yamaha Disklavier的硬件感知作曲系统，通过分布切换整合三者：L系统符号直接选择不同分布范式，而非仅调节参数。四项贡献包括：(1) 四层架构（符号、参数、数值、物理）生成统计显著的段落，效应量达d = 3.70–5.34，经逐层降级与消融实验验证；(2) 硬件抽象层建模速度相关延迟与键位复位约束，确保超人级纹理在可操作范围内；(3) 密度扫面揭示24–30音/秒处出现计算饱和，单域旋律指标失敏，跨域耦合成为必要；(4) 建立收敛点微积分，将节奏赋格几何转化为控制接口，使宏观时间结构触发微观分布切换。所有结果为计算验证，未来拟开展听觉心理实验。系统已在实体Disklavier上部署，实现算法自洽与亚毫秒级软件精度。补充材料：https://www.amanous.xyz。源码：https://github.com/joonhyungbae/Amanous。
原文摘要 · Abstract (English)
The automated piano enables note densities, polyphony, and register changes far beyond human physical limits, yet the three dominant traditions for composing such textures--Nancarrow's tempo canons, Xenakis's stochastic distributions, and L-system grammars--have developed in isolation. This paper presents Amanous, a hardware-aware composition system for Yamaha Disklavier that unifies these methodologies through distribution-switching: L-system symbols select distinct distributional regimes rather than merely modulating parameters within a fixed family. Four contributions are reported. (1) A four-layer architecture (symbolic, parametric, numeric, physical) produces statistically distinct sections with large effect sizes (d = 3.70-5.34), validated by per-layer degradation and ablation experiments. (2) A hardware abstraction layer formalizes velocity-dependent latency and key reset constraints, keeping superhuman textures within the Disklavier's actuable envelope. (3) A density sweep reveals a computational saturation transition at 24-30 notes/s (bootstrap 95% CI: 23.3-50.0), beyond which single-domain melodic metrics lose discriminative power and cross-domain coupling becomes necessary. (4) A convergence point calculus operationalizes tempo-canon geometry as a control interface, enabling convergence events to trigger distribution switches linking macro-temporal structure to micro-level texture. All results are computational; a psychoacoustic validation protocol is proposed for future work. The pipeline has been deployed on a physical Disklavier, demonstrating algorithmic self-consistency and sub-millisecond software precision. Supplementary materials (Excerpts 1-4): https://www.amanous.xyz. Source code: https://github.com/joonhyungbae/Amanous.
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