H2H音乐即兴：一种音乐即兴的通信模型及视听数据集
H2H Music Improv: A Communication Model and Audio-Visual Dataset for Music Improvisation
- Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对现有AI即兴系统缺乏通信意识的问题，通过与专业即兴者协作推导通信模型，并构建首个自由即兴的H2H视听数据集，为AI音乐伙伴设计提供新资源。
AI中文摘要：
当前的实时AI即兴系统缺乏人类音乐家所依赖的通信意识：大多数系统并非将通信作为算法设计的基础，而是通过显式控制和预定义模式，将交互策略事后叠加在生成算法上。这种差距持续存在，部分原因是缺乏与音乐家共同使用的形式化、机器可读的通信模型。为解决这一问题，我们研究了专业音乐家在不受事先讨论或协议约束的自由（非特定风格）即兴中如何进行通信。通过与专业即兴演奏者的协作共设计过程，我们推导得出一种通信模型，该模型（1）捕捉自由即兴演奏者如何协商音乐想法并进入稳定音乐空间，（2）被形式化为机器可读的标注方案。我们还提供了H2H（Human-to-Human）音乐即兴数据集：六小时的专家二重奏即兴视听数据，包含清晰的每位演奏者的音轨，以及每位演奏者对自身意图和对搭档意图感知的标注。据我们所知，这是首个针对自由即兴的此类数据集。通信模型与数据集共同为研究音乐家通信提供了新视角和资源，未来或可用于指导设计具备通信能力的AI音乐伙伴。
英文摘要：
Current real-time AI improvisation systems lack the communication awareness human musicians rely on: rather than treating communication as a foundational algorithm design concern, most systems layer interaction strategies post-hoc onto generative algorithms through explicit controls and predefined modes. This gap persists in part because no formalized, machine-readable communication model with musicians exists. To address this, we study how expert musicians communicate in free (non-idiomatic) improvisation, unconstrained by prior discussion or agreement. Through a collaborative co-design process with expert improvisers, we derive a communication model that (1) captures how free improvisers negotiate musical ideas and enter stable musical spaces, and (2) is formalized as a machine-readable annotation scheme. We further present the H2H (Human-to-Human) Music Improvisation dataset: six hours of audio-visual expert duo improvisations with clean per-player stems and per-player annotations of both their own intentions and their perception of their partner's intentions. To our knowledge, this is the first such dataset for free improvisation. Together, the communication model and the dataset offer a new lens and resource for studying musician communication and may in future inform the design of AI musical partners that communicate by design.
