让AI在合唱中主动参与协调，而非被动回应。
Beyond Call and Response: Modelling Reciprocal Coordination in Human-AI Vocal Ensembles
- 将人机合唱视为耦合动态系统，设计可主动参与的声乐代理架构。
- 在非均等节拍曲目中实现多对多协同，突破传统节奏网格限制。
- 适合研究音乐协作、人机交互与集体智能的学者与开发者。
人类与AI的音乐互动通常表现为响应循环：人类演奏后，系统解析并作出回应、伴奏或安排音乐事件。然而，在无指挥的声乐合奏中，演唱者同时持续相互影响，时间和音高均无固定基准，集体组织依赖于多方间的相互调整。本文将此类合奏视为耦合动态系统，提出一种使声乐代理能主动介入而非仅追踪集体状态的研究架构。部分目标曲目具有规整节拍，而另一些则呈现非等时的时间轮廓，无法简化为节拍网格；后者被视作通用框架的难点案例。该架构连接现场多通道采集、方言与演唱风格感知表示、集体状态推断、声乐生成及现场评估。由此形成的新研究议程不仅关注人工歌手能否同步，更探究其存在如何重构人类的协调方式、领导力、风格与音乐传承。
原文摘要 · Abstract (English)
Musical interaction with AI is often organised as a response loop: a human performs, the system interprets that action, and the system answers, accompanies, or schedules a musical event. Unconducted vocal ensembles pose a different problem. Singers act simultaneously and continuously affect one another; neither timing nor pitch is fixed by a conductor, metronome, accompaniment, score, or tuning source. Collective organisation emerges from many-to-many reciprocal adjustment. This paper frames such ensembles as coupled dynamic systems and proposes a research architecture for vocal agents that enter, rather than merely track, their collective states. Some target repertoires are metrical, while others exhibit non-isochronous temporal contours that cannot be reduced to a beat grid; we treat the latter as a hard case for a general framework. The architecture connects multichannel capture in the field to dialect- and singing-aware representation, collective-state inference, vocal generation, and in-situ evaluation. The resulting agenda asks not only whether an artificial singer can synchronise, but how its presence reorganises human coordination, leadership, style, and musical transmission.
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