Dancing Through Soundscapes: Designing a Low-Cost, Sound-Based Device for Sensing and Interpreting Movement and Dance

Augmenting instruments with sensors and neural network mappings is a well-explored digital musical instrument design approach. While augmentations can create new expressive opportunities, they also exert aesthetic influence and can constrain musicians' gestural language, which, if left unchecked, can lead to technological capture. To examine this, we conducted a study with a professional percussionist, co-developing a gesture mapping toolkit and recording a ten-track album. Drawing on the concept of productive dissonance, our study aimed to hold the musician's aesthetic in tension with technological constraints. This, along with a practice-based reflective approach, supported the development of a continuous gesture recognition method for percussive mapping and surfaced insights into the design process. We identify knowing-when as a form of tacit knowledge that supported productive dissonance, and raise an open question: absent a musician's broader social context, how do we know whether a technology's influence is genuinely supporting their practice?

Human responses to sound depend not only on its semantic content, but also on the evolving spatial relationship between source and listener, conveyed through spatial audio and its temporal dynamics. Modeling human reactions to such dynamic spatial audio is important for character animation, human behavior analysis, and robotics. As of yet, existing audio-driven motion generation methods primarily focus on non-spatial or static acoustic conditions, leaving this important problem largely underexplored. To bridge this gap, we formulate the novel task of dynamic spatial-audio-driven human motion generation and introduce two motion-captured datasets. We introduce the Dynamic Spatial Audio and Motion dataset (DySAM) as the first dataset and benchmark for this task, providing synchronized binaural audio, 3D human motions, and moving sound source trajectories. Given the importance of dynamic spatial cues of sound sources, we further provide the Dynamic Spatial Audio Localization dataset (DySAL), an auxiliary binaural localization dataset that pairs diverse acoustic events with head poses and moving source trajectories to support spatial audio perception. For benchmarking, we propose MOMO for human Motion generation from Moving Sounds, a diffusion-based framework guided by three coupled principles: localization, understanding, and generation. MOMO performs localization by estimating head-centric spatial direction cues of the moving source directly from raw binaural audio, avoiding reliance on ground-truth source coordinates. It performs understanding by extracting frame-wise audio-content representations, and performs generation by conditioning motion diffusion on both semantic features and dynamic spatial cues. Experiments show that MOMO outperforms existing baselines in motion fidelity, audio-motion alignment, and physical plausibility.

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.