Limb velocity variability impacts optimal sound production in marimba performance
Bibliographic record
Abstract
Musicians execute thousands of complex movements during a performance. This study examined the accuracy of such movements in a marimba performance context. Marimba performance is associated with an idealistic performance accuracy component wherein the terminal position of the mallets should strike the bar over the resonator to produce optimal sound. The aims were to investigate how performance tempo impacts mallet accuracy as well as to assess potential kinematic mechanisms underlying such performance. Thirteen percussion majors performed a two-mallet excerpt in slow (110 bpm), intermediate (120 bpm), and fast tempo conditions (130 bpm). Motion tracking was used to monitor the positions and compute velocities of the mallets, hands, wrists, and elbows. Endpoint errors were obtained by comparing the mallet's terminal position to that of the visual target located over the resonator on the marimba's bars. It was hypothesized that lower mallet accuracy would be observed in the fast vs. the slow condition and that mallet accuracy would be driven by altered limb segment velocity relating to tempo condition. Indeed, the mallet accuracy analysis revealed lower accuracy in the fast vs. the slow tempo conditions. Interestingly, velocity variability was greater in the intermediate and fast conditions compared to the slow condition in the left elbow, left wrist, and right hand. This pattern may suggest that limb velocity variability can negatively impact mallet accuracy and thus reduce optimal sound production. Therefore, identifying strategies to target limb velocity during the skill acquisition phase may enhance motor learning in marimba performance.Acknowledgments: * Indicates joint first-authorship; The Canada Foundation For Innovation and the Percussion Department in the Faculty of Music at U of T
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".