Optimizing Acoustic Performance and Structural Integrity of the Oud
Bibliographic record
Abstract
This paper presents a combined numerical and experimental investigation into the vibroacoustic behavior of a traditional oud. An experimental modal analysis was conducted using impact hammer testing to determine the oud’s soundboard’s dynamic characteristics and frequency response function for up to 400[Formula: see text]Hz. Finite element analysis was used to model the oud, incorporating its precise geometry, the wood’s orthotropic material properties, and its interaction with the surrounding air. Validation was performed by matching the numerical and experimental mode shapes and the natural frequencies. Harmonic acoustic analysis examined the oud’s sound pressure level radiation and cavity resonance. Structural–acoustic optimizations were conducted systematically, varying the soundhole’s size, the soundboard’s thickness, and the dimensions of the internal bracing to maximize the acoustics properties while minimizing the structural stress. The effects of these geometric factors on the instrument’s tonal characteristics were quantified. The results provide physical insights into the relationship between the oud’s construction and sound production. The methodology demonstrates a rigorous approach combining simulations and experimentation to comprehensively evaluate and optimize the vibroacoustic behavior of a musical instrument. This fundamental understanding could guide future improvements in the design of ouds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".