Theoretically informed training improves accent production in percussion
Why this work is in the frame
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Bibliographic record
Abstract
Theories of accent production lack empirical data supporting how notes are marked as accented in performance. The present study used percussion performance as a lens to investigate how movements of the performer’s mallets, hands, and wrists contribute to marking notes as accented and how such movements may be refined via training. Experienced percussionists completed a single experimental session where they practiced an excerpt scored for multiple drums. During training, an instructor provided theoretically informed coaching prompts designed to improve performance quality and effectiveness of accent production between successive performances of the excerpt. Motion capture technology measured movements of the mallets, hands, and wrists along specific phases of the accent trajectory: the preparatory upstroke, accent downstroke, post-accent upstroke, and following-note downstroke. Analyses revealed that at post-training, the position of both mallets during the accent downstroke were higher than pre-training. Mallet velocity was also greater in post-training vs. pre-training for the accent downstroke. The average hand position was higher in post-training vs. pre-training for the preparatory upstroke. These changes in movement kinematics coincided with increased effectiveness of accent production in post- vs. pre-training performances, as evaluated by trained judges. The results were interpreted with regards to unique mechanisms that give rise to accent production in relation to mallet and upper-limb movements, and how such mechanisms can be applied towards translating theory into practice for improving accent production in percussion.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it