Modeling of a violin input admittance by direct positioning of second-order resonators
Bibliographic record
Abstract
When approaching violin sound synthesis, the theoretical advantage of body modeling by finite difference or finite element paradigms comes from parameterizing the model by geometry and material properties. However, difficulties in representing the complexity of physical phenomena taking place have kept such approaches from raising more success, due especially to limited modeling accuracy and high computational cost. Conversely, the design of digital filters from admittance measurements, although generally offering a less meaningful parameterization, represents a more affordable technique as it provides significant fidelity at lower computational cost. Within digital filter formulation applied to the violin, modal representations can be considered as among the most physically pertinent, since vibration modes defining the timbre signature are in general observable from admittance measurements. This work introduces a technique for designing violin passive admittances by direct, non-uniform positioning of second-order resonators. Starting from admittance measurements, second-order resonator parameters are designed so that desired modes are modeled from frequency-domain data. Positve real models providing significant accuracy at low order are obtained from second-order resonator parameter fits. As an example, a two-dimensional input admittance is designed from measurements, so that it can be used for a digital waveguide model to include a control for bowing angle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".