ELECTRO/ACOUSTIC SOUNDPAINTING RECOGNITION: COORDINATION AND COMMUNICATION BETWEEN HUMAN AND MACHINE PERFORMERS
Bibliographic record
Abstract
This paper presents an approach to Soundpainting-based gesture recognition, using a machine learning framework developed in Python. This project builds upon our previous work, which focused on incorporating computational musical agents into an electroacoustic performance ensemble. In so doing, the system enables direct control of machine agents via gesture recognition. While specifically designed for thee Doug Van Nort Electro-Acoustic Orchestra (EAO), our approach offers flexible training and model generation for other composers and Soundpainting environments. At the time of writing the system is capable of detecting 41 Soundpainting gestures, and is modular and extensible to allow for expansion of the gesture dictionary. Gesture recognition is completed using four separate models, each targeting a separate vertical slice of the Soundpainter. Additionally, each gesture is broken down into “gesture components” (one or two combined individual hand gestures), which taken together make up a full combination gesture. Technical details and system design justifications are presented for model training, gesture recognition, and accompanying Max-based control patches. Initial testing results are presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".