A detailed study of the impedances of mouthpieces, flared bells, and curved pipes in brass instruments
Bibliographic record
Abstract
The input impedance of a brass instrument provides information about quality and playing characteristics, such as intonation and response. Modeling the input impedance of an instrument with the transfer matrix and finite element methods based on its geometry is useful, as the calculations can be performed without a physical prototype. Previous work has determined that both the transfer matrix and finite element methods can determine the properties of the impedances of mouthpieces and flaring bells. These results will be more closely examined and compared with regard to relative peak locations. At present, the transfer matrix method can only be used for conical or cylindrical segments along a straight axis, so it is useful to add curvature as a parameter of the calculation. An adaptation to the transfer matrix calculation for curved pipes will be presented. Transfer matrix and finite element calculations will be compared with each other and with impedance measurements. The ultimate goal of this work is to accurately calculate the input impedance of a realistic brass instrument, with curved pipes, valves, water keys, and slides. This will allow for improvement of existing instruments and development of new instruments and instrument parts exhibiting particular acoustical characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".