A comparison of modeled and measured impedance of brass instruments and mouthpieces
Bibliographic record
Abstract
The impedance of a brass instrument has an important influence on its playability and sound timbre. The geometry of the mouthpiece has various features, such as the cup volume and shape, opening diameter, and length, that determine the characteristics of the overall impedance of the instrument-mouthpiece combination. Brass instruments, and especially mouthpieces, are designed for specific purposes, and horns or mouthpieces are chosen depending on the musical requirements. In order to investigate the relationship between the physical parameters and the impedances of instruments and mouthpieces, they have been modeled with transfer matrix and finite element model techniques, and the results are compared with impedance measurements of instruments, mouthpieces, and combinations of instruments and mouthpieces. Trumpets, flugelhorns, (French) horns, trombones, and the corresponding mouthpieces have been used. A detailed analysis of the estimation of the viscothermal losses has been performed, as the loss estimation in the narrow throat of the mouthpiece and in the flaring part of the brass instrument bell departs from the ordinary transfer matrix calculations. The effect of varying the physical parameters of mouthpieces and instruments is investigated by means of impedance considerations and sound synthesis, and the resulting influences on intonation, playability, and timbre are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".