Computational acoustic methods for the design of woodwind instruments
Bibliographic record
Abstract
This thesis presents a number of methods for the computational analysis of woodwind instruments. The Transmission-Matrix Method (TMM) for the calculation of the input impedance of an instrument is described. An approach based on the Finite Element Method (FEM) is applied to the determination of the transmission-matrix parameters of woodwind instrument toneholes, from which new formulas are developed that extend the range of validity of current theories. The effect of a hanging keypad is investigated and discrepancies with current theories are found for short toneholes. This approach was applied as well to toneholes on a conical bore, and we conclude that the tonehole transmission matrix parameters developed on a cylindrical bore are equally valid for use on a conical bore.A boundary condition for the approximation of the boundary layer losses for use with the FEM was developed, and it enables the simulation of complete woodwind instruments. The comparison of the simulations of instruments with many open or closed toneholes with calculations using the TMM reveal discrepancies that are most likely attributable to internalor external tonehole interactions. This is not taken into account in the TMM and poses a limit to its accuracy. The maximal error is found to be smaller than 10 cents. The effect of the curvature of the main bore is investigated using the FEM. The radiation impedance of a wind instrument bell is calculated using the FEM and compared to TMM calculations; we conclude that the TMM is not appropriate for the simulation of flaring bells.Finally, a method is presented for the calculation of the tonehole positions and dimensions under various constraints using an optimization algorithm, which is based on the estimation of the playing frequencies using the Transmission-Matrix Method. A number of simple woodwind instruments are designed using this algorithm and prototypes evaluated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".