A FREQUENCY-BASED CHARACTERIZATION OF SPICCATO BOWING IN VIOLIN PERFORMANCE<sup>1,2</sup>
Bibliographic record
Abstract
Performance of instrumental music requires high precision and the automisation of motor control to free the performer to focus on the artistic outcome. To acquire this high skill, training is experience-based, involves one-on-one instruction, and requires long hours of repetitive practice. This approach is consistent with a traditional model of vocational apprenticeship. Practice habits and long hours associated with training have been identified as sometimes contributing to high rates of vocational injury among musicians. This study explores violin performance, identifying generalizable perceptual markers to bridge the gap between science and experience in pedagogical methodology. Kinematic data were collected using 3-D motion capture. Dynamic modeling was used to specify internal loads. Eleven professional-level musicians were tested, ranging in age from 21 to 47 years (M = 36 yr., SD = 6). The study identified several motor-learning markers, speed-dependent motor control phases (increasing effort, optimization, and approaching physiological limits), string-dependent motor control, and an unexpected sympathetic resonance between the two arms, notwithstanding their very different functions. This study suggests that instrumental performance could be aided by identifying markers related to musical outcomes, performers' perceptions, and motor skill acquisition.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".