Amanous: Distribution-Switching for Superhuman Piano Density on Disklavier

TL;DR

Amanous is a novel hardware-aware system for Yamaha Disklavier that unifies traditional composition methods through distribution-switching, enabling superhuman piano textures with validated effects and precise control.

Contribution

It introduces a multi-layer architecture, a hardware abstraction layer, and a control interface for distribution-switching, advancing automated piano composition beyond existing methods.

Findings

Statistically distinct musical sections with large effect sizes

Identified a saturation transition at 24-30 notes/sec

Demonstrated algorithmic self-consistency on physical Disklavier

Abstract

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.