Gesture Analysis of radiodrum data
Bibliographic record
Abstract
The radiodrum is a virtual controller/interface that has existed in various forms since its initial design at Bell Laboratories in the 1980’s, and it is still being developed. It is a percussion instrument, while at the same time an abstract 3D gesture/position sensor. There are two main modalities of the instrument that are used by composers and performers: the first is similar to a percussive interface, where the performer hits the surface, and the instrument reports position (x,y,z) and velocity (u) of the hit; thereby it has 6 degrees of freedom. The other mode, which is unique to this instrument (at least in the domain of percussive interfaces), is moving the sticks in the space above the pad, whereby the instrument also reports (x,y,z) position in space above the surface. In this paper we describe techniques for identifying different gestures using the Radio Drum, which could include signals like a circle or square, or other physically intuitive gestures, like the pinch-to-zoom metaphor used on mobile devices such as the iPhone. Two approaches to gesture analysis are explored. The first one is based on feature classification using support vector machines and the second is using Dynamic Time Warping. By allowing users to interact with the system using a complex set of gestures, we have produced a system that will allow for a richer vocabulary for composers and performers of electro-acoustic music. These techniques and vocabulary are useful not only for this particular instrument, but can be modified for other 3D sensors.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".