Experimental analysis of cello string types: influence on playability and tonal characteristics using Schelleng diagrams
Bibliographic record
Abstract
This study experimentally assesses the influence of cello string types on playability and tonal characteristics using a robotic bowing device and a custom monochord setup.Eight custom-made G2 cello strings, each defined by unique combinations of core materials, windings, and nominal tensions, were systematically tested.High-resolution Schelleng diagrams were constructed from steady-state signals across various bow forces, bow-bridge distances, and bow speeds.Analyses included bow force limits, spectral centroid, pitch flattening, and anomalous low frequencies.Results revealed similar regions of Helmholtz motion, with deviations from Schelleng's classical theory aligning with recent research.Pitch flattening occurred mainly at high bow forces and lower bow speeds, with deviations up to 60 cents below nominal pitch, while spectral centroid analysis highlighted a significant correlation between bending stiffness and perceived tonal brightness.Minimal within-type variability was noted; however, tonal deviations due to string settling were substantial, underscoring the importance of play-in periods.Although playability differences across tension groups were minor, tonal descriptors, notably spectral centroid, displayed clearer sensitivity to material composition.Future studies should explore broader string variations and detailed harmonic content to enhance statistical robustness and generalizability.This research was funded in whole or in part by the Austrian Science Fund (FWF) [P34852-N].
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".