Preservation and Study of Historical Guitars Museu de la Musica de Catalunya: Methodology Proposal to Play the Unplayable
Bibliographic record
Abstract
WoodMusick project is a multi-disciplinary COST action research project. The main goal of the project is to study museum instruments and preserve their physical and acoustical properties. This paper aims to contribute the project by studying historical guitars in Museu de la Musica, Catalunya, as well as to propose a global measurement methodology for prediction of acoustical properties of guitars during the design stage. Two historical classical guitars, Torres 625 from Antonio de Torres and Labrador from the romantic period are studied, in addition to a test guitar. Steady-state and impulse responses are recorded and spectral and physical properties are extracted from recordings. The results are classified with WEKA by ESSENTIA descriptors and two guitars are successfully identified and distinguished from each other. Extracted features and physical properties are observed in the latter stage and sound feature estimations are proposed. Besides the multidisciplinary nature of the study is already a challenging task, the main obstacle turned out to be to build a methodology that ensures the safety of the instrument while collecting the necessary data. The results are promising for building a dataset with a proposed methodology to predict acoustical features of guitars in the design stage as well as creating an archive for historical instruments and monitor their physical state and its effect on sound features in time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".