Detecting overblown flute fingerings from the residual noise spectrum
Bibliographic record
Abstract
Producing a tone by increasing the blowing pressure to excite a higher frequency impedance minimum, or overblowing, is widely used in standard flute technique. In this paper, the effect of overblowing a fingering is explored with spectral analysis, and a fingering detector is designed based on acoustical knowledge and pattern classification techniques. The detector performs signal analysis of the strong broadband signal, that is, spectrally shaped by the pipe impedance, and measures the spectral energy during the attack around multiples of the fundamental frequency sub-multiples over the first octave and a half. It is trained and evaluated on sounds recorded with four expert performers. They played six series of tones from overblown and regular fingerings, with frequencies that are octave- and non-octave-related to the playing frequency. The best of the four proposed sound descriptors allows for a detection error below 1.3% for notes with two and three fingerings (C(5), D(5), C(6), and Cmusical sharp(6)) and below 14% for four (E(6)) or five fingerings (G(6)). The error is shown to dramatically increase when two fingerings' impedance become too similar (E(6) and A(4) and G(6) and C(5)).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".