A study of impedance of brass instruments and mouthpieces—Comparison of models and measurements
Bibliographic record
Abstract
The impedance of a brass instrument has an important influence on the frequencies of the notes that can be played and on the timbre of the sound. The shape 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 many brass players own several different horns or mouthpieces, and choose which to use depending on their particular musical requirements at the time. In order to investigate the relationship between the physical parameters of instruments and mouthpieces and the resulting impedance, brass instruments and mouthpieces have been modeled with transfer matrix techniques, and the results are compared with impedance measurements of the instruments alone, the mouthpieces alone, and combination of instruments and mouthpieces. Trumpets, flugelhorns, horns, trombones, and the corresponding mouthpieces have been used for this study. The mouthpiece-instrument combination has been investigated in terms of intonation, playability, and timbre. The question of whether (and why) some mouthpieces are more suited to certain instruments and certain playing styles is investigated as well as the effect of varying the physical parameters of mouthpieces and instruments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".