New algorithms and technology for analyzing gestural data
Bibliographic record
Abstract
We describe the ways in which we are analyzing gestural data as if they were an audio signal, and applying this technique to the radio drum, a novel three-dimensional controller that one of the authors uses regularly for concert performances. The radio drum uses capacitive sensing; a radiofrequency voltage source is conducted from the performer's mallets or sticks, and is received on the drum surface beneath. The two sticks are differentiated by using different frequencies for each one. Signals from each stick represent typical gestures. The signal processing includes two key steps. The first step is to capture all of the subtle motions associated with these gestures, so that the instrument responds in a sensitive manner to the performer's expressive technique. We seek to capture details of the entire motion represented by the x,y,z signals versus time, not just the velocity of a strike. The second step is to map the gesture signal to control musical events. This mapping can take many forms, depending on the creative ideas of the composer and/or performer.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".