Influence of melodic emphasis, texture, salience, and performer individuality on performance errors
Bibliographic record
Abstract
We investigated the influence of melodic emphasis, musical texture, musical salience, and performer individuality on the distribution and frequency of errors in keyboard performance. Eight performers recorded different interpretations of two short Baroque organ pieces of contrasting texture (homophonic versus polyphonic style). Melodic emphasis affected the distribution of performance errors according to the location of the part intended as melody, whereas musical texture influenced the type of errors. The effect of musical salience was examined by inviting 16 performers to record a Bach organ fugue. Error rates were lower for notes belonging to recurring musical motives than for non-motivic passages, and for outer voices compared to inner voices. These results are consistent with error detection studies showing that errors in inner voices or unfamiliar melodies are less perceptually salient, suggesting that the error likelihood is inversely related to a note’s degree of perceptual and musical salience. Across all three pieces, error patterns were more consistent in within-performer comparisons than between-performer comparisons of recordings of the same piece, implying that error patterns are indicative of individual differences in the interpretation of musical structures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".