Bibliographic record
Abstract
This dissertation proposes an interdisciplinary approach for the study of the timbre of the classical guitar. We start by identifying the static; control parameters of timbre, relating to the structural components of the guitar and the dynamic control parameters of timbre, relating to the gestures applied by the performer on the instrument. From the plucked string physical model (obtained from the transverse wave equation), we derive a digital signal interpretation of the plucking effect which is a comb filtering. Then we investigate how subjective characteristics of sound, like timbre, are related to gesture parameters. The starting point for exploration is an inventory of verbal descriptors commonly used by professional musicians to describe the brightness, the colour, the shape and the texture of the sounds they produce on their instruments. An explanation for the voice-like nature of guitar tones is proposed based on the observation that the maxima of the comb-filter-shaped magnitude spectrum of guitar tones are located at frequencies similar to the formant frequencies of a subset of identifiable vowels. These analogies at the spectral level might account for the origin of some timbre descriptors such as open, oval, round, thin, closed, nasal and hollow, that seem to refer to phonetic gestures. In a experiment conducted to confirm these analogies, participants were asked to associate a consonant to the attack and a vowel to the decay of guitar tones. The results of this study support the idea that some perceptual dimensions of the guitar timbre space can be borrowed from phonetics. Finally, we address the problem of the indirect acquisition of instrumental gesture parameters. Pursuing previous research on the estimation of the plucking position from a recording, we propose a new estimation method based on an iterative weighted least-square algorithm, starting from a first approximation derived from a variant of the autocorrelation func
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".