Estimating the clarinet mouthpiece reflection from measurement and the instrument's produced sound
Bibliographic record
Abstract
In this work, a method is presented for estimating the reflection off the clarinet mouthpiece, using a priori measurement of the bell, and post processing of the instrument's produced sound. A previously introduced measurement technique is used to obtain measurement of clarinet bell and transmission filters. In addition to these elements, however, the round-trip propagation loss in the clarinet bore and bell also includes wall loss and mouthpiece reflection. Though the former is accurately modeled theoretically, assuming the clarinet bell is close to cylindrical, the mouthpiece is more difficult to measure, both because of a supposed oscillating reed, and because the required placement of a measurement device would obstruct the mouthpiece's characteristic reflection. The lumped round-trip loss filter in the bore is estimated from the clarinet signal by first considering the signal's periodic structure. After taking the signal's autocorrelation, which preserves its periodicity and naturally provides the beginning of the period, the round-trip filter is iteratively estimated by constructing an optimization function from the first and second phases of the autocorrelation sequence. Once the round-trip loss is estimated, the mouthpiece reflection may be extracted by removing the effect of the other known comprising filter elements.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".