用量子启发方法生成音乐和声，兼顾自由与可控性。
Designing Maintainable Hybrid Generative Systems: A Quantum-Inspired Approach to Automated Music Harmony Generation
- 结合量子启发搜索与规则优化，动态探索和声候选。
- 无需训练数据，提升结构一致性与可预测性。
- 适合音乐生成系统开发者与作曲研究者参考。
本文提出并评估了一种可维护的混合生成架构，用于从旋律自动生成音乐和声。该系统结合量子启发的重叠旋律上下文候选探索与显式规则优化，平衡生成灵活性与结构控制。通过结构连贯性、功能一致性、和声相似性及鲁棒性等可复现指标进行评估。结果表明，该方法在保留调性结构与终止行为的同时，支持多种有效和声实现；优化层显著提升结构连贯性、稳定性和可预测性，且无需训练语料。研究证明，在信息系统开发框架下，可透明、可控地系统设计并评估混合生成系统。
原文摘要 · Abstract (English)
This paper presents the design and evaluation of a maintainable hybrid generative architecture for automated music harmony generation from melody. The proposed system combines quantum-inspired candidate exploration over overlapping melodic contexts with explicit rule-based optimization to balance generative flexibility and structural control. The architecture is evaluated using explicit and reproducible metrics covering structural coherence, functional agreement, harmonic similarity, and robustness. The results show that the proposed approach produces harmonizations that preserve tonal structure and cadential behavior while allowing multiple valid harmonic realizations. Furthermore, the optimization layer improves structural coherence, stability, and predictability without requiring a training corpus. The study demonstrates that transparent and controllable hybrid generative systems can be systematically designed and evaluated within the context of Information Systems Development.
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