Indirect acquisition of flutist gestures: a case study of harmonic note fingerings
Bibliographic record
Abstract
The indirect acquisition of musicians' gestures consists in retrieving information about gestures from the sound. Inspired by previous studies investigating clarinetists' gestures, we focus on the flute, which features numerous playing modes and a rich palette of sounds. Our goal is to provide musicians with guidelines for using indirect acquisition. For a variety of playing modes, we collected information about gestures, body parts involved, and effects on the acoustical signal. We then designed signal processing algorithms for recognizing which gesture is involved in a given sound; later we will quantize related movement by combining direct acquisition, indirect acquisition and motion capture. Focusing on harmonic note fingerings, a first experiment investigated octave-related harmonic note fingerings with 1 nonexpert performer (ff dynamics and normal articulation). A target F0-guided PCA on the amplitude of the first six harmonics of F0/4 from 1 semi-tone frequency bands retrieved the correct fingering with two principle components in all trials. A second experiment investigated octave and non-octave related harmonic note fingerings with four expert performers. A target F0-guided PCA on the sum of energies around iF0/k (k=2,...,6; i=1,...,12) provided wider clusters, making more difficult to retrieve the correct fingering in some specific cases that we will discuss.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".