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Record W4403341648 · doi:10.1142/s0219455426500392

Optimizing Acoustic Performance and Structural Integrity of the Oud

2024· article· en· W4403341648 on OpenAlexaff
Elshaikh Mohamed, Amine Sassi, Mohamed Gharib, Sadok Sassi

Bibliographic record

VenueInternational Journal of Structural Stability and Dynamics · 2024
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStructural integrityAcousticsStructural engineeringComputer scienceReliability engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.235
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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