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Record W2048923014 · doi:10.1121/1.3685481

Characterization of woodwind instrument toneholes with the finite element method

2012· article· en· W2048923014 on OpenAlexafffund
Antoine Lefebvre, Gary Scavone

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2012
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCentre for Interdisciplinary Research in Music Media and Technology
KeywordsFluteFinite element methodDiscontinuity (linguistics)AcousticsRange (aeronautics)Transfer matrixMathematicsMaterials scienceStructural engineeringComputer scienceMathematical analysisPhysicsComposite materialEngineering

Abstract

fetched live from OpenAlex

A method is proposed to determine the transfer matrix parameters of a discontinuity in a waveguide with the finite element method (FEM). This is used to characterize open and closed woodwind instrument toneholes and develop expressions for the shunt and series equivalent lengths. Two types of toneholes are characterized: Unflanged toneholes made of thin material, such as found on saxophones and concert flutes, and toneholes drilled through a thick material, such as found on most instruments made of wood. The results are compared with previous tonehole models from the literature. In general, the proposed expressions provide a better fit across a wide range of frequencies and tonehole sizes than previous results. For tall toneholes, the results are in general agreement with previous models. For shorter tonehole heights, some discrepancies from previous results are found that are most important for larger diameter toneholes. Finally, the impact of a main bore taper (conicity) on the characterization of toneholes was investigated and found to be negligible for taper angles common in musical instruments.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations34
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207