Evaluation of two control parameters of trumpet players as function of sound features.
Bibliographic record
Abstract
Experiments were performed by five trumpet players with the same trumpet and mouthpiece. For each subject, two control parameters are measured: mouth-pressure and force applied by the lips on the mouthpiece. The sound is also recorded. The players are asked to play sustained notes at different pitch and sound levels. We calculate fundamental frequency, sound pressure level, and some spectral features: noisiness, spectral roll-off, etc. Although little mouth-pressure variability as function of pitch and dynamic appears between subjects, clear differences were obtained in terms of the force applied on the mouthpiece. Therefore, we suggest to focus on the link between sound features and the ratio of these two control parameters measured, sort of “gesture estimator.” The representation of the ratio as function of the sound features shows a good classification of musicians. Moreover, in some specific cases such as noisiness, the relation seems independent from the players. These observations suggest that these two control parameters could be efficient to characterize players’ embouchure and to access some sound characteristics without knowledge of the mechanical characteristics of the lips. These results will be described and discussed, and direction for future works suggested. [Consonnes project, supported by the French Research Agency.]
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".