Bibliographic record
Abstract
The focus of this work is to identify the tonehole configuration or “fingering'' applied by a player during performance, using only the signal recorded at the bell. Because of a player can use alternate fingerings/overblowing to produce a given frequency, detecting the sounding pitch only reduces the possible candidates—it does not produce a unique result. Several recordings of a professional saxophonist playing notes using all fingerings are considered, and several higher level features are explored for distinguishing between a fundamental and an overblown note. In the latter case, it is observed that during the attack portion of the note, the spectral centroid is usually lower, there is greater inharmonicity and increased pitch instability. Combining these heuristics with the detection of subharmonics has yielded excellent results in detecting overblown notes. With the possible fingerings being greatly reduced by this preprocessing, more computationally expensive statistical methods may be employed for a more accurate estimation of the actual fingering applied. To this end, the recorded sound is calibrated to that produced by a reed model coupled to a waveguide that is informed by an acoustic measurement of the player's saxophone configured with each usable fingering.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".