用低成本AI硬件打造可移动的音乐创作工具，支持艺术家自由探索新演奏方式。
Opening the Design Space: Two Years of Performance with Intelligent Musical Instruments
- 基于单板电脑与MIDI连接，用收集的艺术家数据训练小模型。
- 两年实证发现快速输入交错是新共创策略，小数据模型可便携复用。
- 适合音乐家、创作者和声音艺术爱好者进行即兴实验与跨媒介融合。
机器生成符号化音乐与数字音频是热门方向，但集成生成式AI的数字乐器仍较少。现有音乐AI工具缺乏艺术家中心设计，难以融入实际演奏或实验场景。本文提出一种基于单板计算机的低成本生成式AI乐器平台，通过MIDI连接其他设备。模型使用艺术家采集的数据，在普通计算机上训练完成。论文基于两年的亲身艺术研究，展示五个已实现并测试的乐器实例。结果表明：（重）映射可替代重新训练以探索人机交互；快速输入交错是一种新型共创策略；小数据模型具备可迁移性，适合作为便携设计资源；廉价硬件显著降低参与门槛。该工作为艺术家提供了在智能乐器中探索新互动与表演形式的可能性。
原文摘要 · Abstract (English)
Machine generation of symbolic music and digital audio are hot topics but there have been relatively few digital musical instruments that integrate generative AI. Present musical AI tools are not artist centred and do not support experimentation or integrating into musical instruments or practices. This work introduces an inexpensive generative AI instrument platform based on a single board computer that connects via MIDI to other musical devices. The platform uses artist-collected datasets with models trained on a regular computer. This paper asks what the design space of intelligent musical instruments might look like when accessible and portable AI systems are available for artistic exploration. I contribute five examples of instruments created and tested through a two-year first-person artistic research process. These show that (re)mapping can replace retraining for discovering AI interaction, that fast input interleaving is a new co-creative strategy, that small-data AI models can be a transportable design resource, and that cheap hardware can lower barriers to inclusion. This work could enable artists to explore new interaction and performance schemes with intelligent musical instruments.
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