Sound production in four mallet marimba performance: The role of limb velocity variability
Bibliographic record
Abstract
Sound production in a percussion context like marimba results from using two mallets held in each hand to strike a unique bar/target location. If the mallets contact the bar above the resonator below, optimal sound production occurs. This study examined mallet accuracy to further understand mechanisms underlying motor control in marimba performance. Thirteen percussionists played a four-mallet excerpt in three tempo conditions including slow, intermediate, and fast. Mallets were held proximal to the pinky (mallet 1: left hand, mallet 4: right hand) and thumb (mallet 2: left hand, mallet 3: right hand). Motion tracking measured movements and velocities of the mallets, wrists, and elbows. Instructions were to terminate each mallet's movement onto a visual target located on the marimba's bars above the resonators. Contrasting each mallet's terminal position with its unique target location when all four mallets contacted the marimba simultaneously (i.e., playing chords) assessed mallet accuracy. Velocity analyses examined the variability of upper-limb and mallet movements. It was hypothesized that increases in limb velocity variability would negatively impact mallet accuracy. The results showed that accuracy was lowest in the fast vs. the intermediate and slow conditions within the outer mallets (i.e., 1, 4). In both elbows and wrists, velocity variability was greater in the fast vs. the slow condition. It may be surmised that increasing velocity variability of limb movements reduces mallet accuracy resulting in suboptimal sound production in the outer mallets specifically. Motor skill acquisition in marimba performance may be facilitated by emphasizing temporal control in the upper-limbs.Acknowledgments: 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".