Visualization feedback for musical ensemble practice: a case study on phrase articulation and dynamics
Bibliographic record
Abstract
We consider the possible advantages of visualization in supporting musical interpretation. Specifically, we investigate the use of visualizations in making a subjective judgement of a student's performance compared to reference "expert" performance for particular aspects of musical performance-articulation and dynamics. Our assessment criteria for the effectiveness of the feedback are based on the consistency of judgements made by the participants using each modality, that is to say, in determining how well the student musician matches the reference musician, the time taken to evaluate each pair of samples, and subjective opinion of perceived utility of the feedback. For articulation, differences in the mean scores assigned by the participants to the reference versus the student performance were not statistically significant for each modality. This suggests that while the visualization strategy did not offer any advantage over presentation of the samples by audio playback alone, visualization nevertheless provided sufficient information to make similar ratings. For dynamics, four of our six participants categorized the visualizations as helpful. The means of their ratings for the visualization-only and both-together conditions were not statistically different but were statistically different from the audio-only treatment, indicating a dominance of the visualizations when presented together with audio. Moreover, the ratings of dynamics under the visualization-only condition were significantly more consistent than the other conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".