Flute ‘‘breath support’’ perception and its acoustical correlates
Bibliographic record
Abstract
Music educators and performers commonly refer to ‘‘breath support’’ in flute playing, yet the term ‘‘support’’ is neither well-defined nor consistently used. Different breathing strategies used by professional flautists who were instructed to play with and without support were previously identified by the authors. In the current study, 14 musical excerpts with and without support were recorded by five professional flautists. Eleven professional flautists listened to the recordings in a random order and ranked (1 to 6) how much of the following sound qualities they judged to be in each example: support, intonation, control and musical expressiveness. Answers to the test showed that musical expressiveness was associated more closely with the supported excerpts than the answers about support itself. The ratings for each sound quality were highly intercorrelated. Acoustical parameters were analyzed (frequency and centroid variation within each note) and compared with the results of the perception test in order to better understand how the acoustical and psychological variables were related. The acoustical analysis of the central part of the notes did not show evident correlation with the answers of the perception test. [Work funded by the Social Sciences and Humanities Research Council of Canada.]
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".