Unique Control of Upstrokes and Downstrokes Yields Expressive Dynamics in Percussion
Bibliographic record
Abstract
Sixteen right-handed percussionists performed a musical excerpt under crescendo and decrescendo conditions to explore kinematic and directional motor control strategies in percussion dynamics. Motion capture technology measured mallet and hand movements to analyze peak mallet/hand height and velocity for each stroke, as well as average mallet/hand position and velocity during upstrokes (mallet trajectory from playing surface to peak height) and downstrokes (trajectory from peak height to playing surface). These measures assessed execution and directional control, respectively. Results showed that peak mallet heights increased from notes 1-4 during crescendos and decreased over the same range during decrescendos, coinciding with increased and decreased peak hand velocity. During crescendos, the left mallet and hand were consistently elevated higher above the playing surface than the right. Within the right hand effects were localized to the velocity domain. For upstrokes, hand velocity was lower in crescendos versus decrescendos, while velocity was higher for downstrokes in crescendos. These findings indicate distinct motor control strategies contributing to the directional control and execution of sound-producing movements, emphasizing limb-specific mechanisms that could inform percussion pedagogy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".