The hypothesis is that control and agency is distributed by machine learning’s algorithms, in which designer and performer, have to navigate through the feedback system made of gestures, latent space and spatialized sound.
It is concluded that exposing the data provenance and generative processes of AI systems can help transform them from opaque tools into intelligible musical partners and that transparency is an important design factor for creative agency in intelligent instruments.
This study conducted a study with a professional percussionist, co-developing a gesture mapping toolkit and recording a ten-track album, which supported the development of a continuous gesture recognition method for percussive mapping and surfaced insights into the design process.
Jordie Shier, Teresa Pelinski, C. Saitis et al.· 0 citations
This paper examines collaborative performance with AI-augmented instruments through a practice-led experiment involving two musicians, and analyses the agency at play to unpack how creative control is distributed between human and machine.
Halla Steinunn Stefánsdóttir, Robert Ek, Thor Magnusson· 0 citations
This chapter proposes material explainability as a range of activities and artifacts that transform AI models into accessible and inclusive design materials in the workspace of artists, designers, and makers to enable sustained creative practices with AI.
S. Zheng, Anna Xambó Sedó, Nick Bryan-Kinns· arXiv.org· 0 citations
By re-considering what counts as signal in augmented instruments that use machine-learning, this work contributes to NIME discussions on affordances, agency, and relational instrument design, fore-grounding co-agency grounded in responsiveness and emergent behaviour.
The proposed RL-based dynamic control system successfully transforms score elements into performance actions which enable robots to deliver expressive music performances.
