Simulating different upstream coupling conditions on an artificial trombone player system using an active sound control approach
Bibliographic record
Abstract
Recent research suggests that the ability to finely tune vocal-tract resonances during trombone playing may constitute an important aspect of performance expertise. Artificial player systems, designed to reproduce the behavior of a real player, often neglect this component by not providing any control of upstream resonances. However, they offer great experimental platforms for quantitative studies on sound production mechanisms, allowing independent adjustment of certain control parameters. An active sound control method was designed to improve high tone support and investigate different conditions of coupling between the artificial lips, the downstream air-column, and the upstream cavity during sustained tones played by an artificial valve-trombone player system. Upstream input impedance at the fundamental frequency was controlled through real-time adjustment of the phase and amplitude ratio between the acoustic pressure generated on both sides of the lips. The phase difference between the upstream and downstream pressures was swept linearly while maintaining different conditions of upstream energy and fixed trombone fingering. Observations during this procedure included: (1) significant fundamental frequency variations in the neighborhood of a downstream impedance peak; and (2) variation of the downstream energy and optimal phase tuning with regard to the mechanical efficiency of the lip-valve system suggested at the energy maximum.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".