Human-Machine Co-creation in an Electroacoustic Ensemble Performance Context
Bibliographic record
Abstract
This paper investigates the outcomes of incorporating computational agents into a telematic electroacoustic performance ensemble. The ensemble is directed by a gestural language developed by the second author, building upon the practice of "Soundpainting". These gestures communicate intended actions for both human and machine players, directed towards formal, relational, temporal, rhythmic, and timbral qualities of sound. The computational agents are expanded upon from an established co-creative system (Dicy2) in order to align with these gesture-based sound/music concepts, and design considerations for this performance context are presented. In play sessions, the agents are played via a control interface by the first author, following the ensemble conducting gestures. Reflections from human participants following these play sessions are reported. These comments provide a range of characterizations, qualitative assessments, and performance experiences with the machine voices. Subsequent discussion engages with creativity in machine learning systems, structural parallels between gestural conducting and the agent audio sequence generation, and incorporation of computational agents within a telematic performance context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".