让画作能感知触摸并发出立体声音，实现人与艺术的实时互动。
Sounding Canvas: Embedding Algorithms in Networked, Sensorial Sound Art
- 通过嵌入传感器与算法，将触控转化为空间化声音。
- 使用CNN离线映射定义声学特征，LSTM在线决策提升交互体验。
- 适合关注交互艺术、算法感知与分布式创作的创作者和研究者。
Sounding Canvas 将绘画转化为触觉响应的多模态装置，通过在画布中嵌入电容式传感器、实时决策模型及网络连接，使触碰触发仿佛来自画作本身的立体声音。该作品从物理层面将算法隐藏于艺术品背后，从感知层面采用离线视觉-听觉映射将画作特征与声音描述符对齐，从表演层面则通过在线事件管理器（高阶马尔可夫模型与基于LSTM的策略）实现对访客及远程画布的实时互动。文中阐述了艺术理念与技术实现，结合基于CNN的离线映射构建声学词汇表，并设计两个在线模型平衡响应性与引导探索。系统通过行为而非代码展现算法存在，网络化使单点触碰演变为分布式共同创作，引发对嵌入式算法艺术中作者权、自主性与评价标准的思考。
原文摘要 · Abstract (English)
Sounding Canvas turns painting into a touch-responsive multimodal installation by embedding capacitive sensors, real-time decision models, and networking inside the canvas. Touches trigger spatialised sounds that appear to emanate from the painting itself. The work embeds algorithms physically, as sensing and computation concealed behind the artwork; perceptually, through an offline visual-to-sonic mapping that aligns a painting's features with sound descriptors; and performatively, through online models that shape live interaction with visitors and with remote canvases over a network. We describe the artistic rationale and technical implementation, combining a CNN-based offline mapping that defines the sound vocabulary with two online event managers, a higher-order Markov model and an LSTM-based policy, that balance responsiveness with guided exploration. We discuss how these layers make algorithms perceptible through behaviour rather than code, how networking transforms solitary touch into distributed co-authorship, and how the system raises questions of authorship, agency, and evaluation in embedded algorithmic artworks.
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