Wave separation in the trumpet under playing conditions and comparison with time domain finite difference simulation
Bibliographic record
Abstract
Wave separation within a trumpet is presented using three high pressure microphones to measure pressure waves within the curved, constant cross-section tuning slide of the instrument while the instrument was being played by a virtuoso trumpet player. A closer inter-microphone spacing was possible in comparison to previous work through the use of time domain windowing on non-causal transfer functions and performing wave separation in the frequency domain. Time domain plots of the experimental wave separation were then compared to simulations using a physical model based on a time domain finite difference simulation of the trumpet bore coupled to a one mass, two degree of freedom lip model. The time domain and frequency spectra of the measured and synthesized sounds showed a similar profile, with the sound produced by the player showing broader spectral peaks in experimental data. Using a quality factor of 5 for the lip model was found to give greater agreement between the simulated and experimental starting transients in comparison to the values in the range 1-3 often assumed. Deviations in the spectral content and wave shape provide insights into the areas where future research may be directed in improving the accuracy of physical modeling synthesis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".