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Record W3138728065 · doi:10.1121/10.0003814

Acoustical modeling of the saxophone mouthpiece as a transfer matrix

2021· article· en· W3138728065 on OpenAlexafffund
Song Wang, Esteban Maestre, Gary Scavone

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2021
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMouthpieceAcousticsTransfer functionElectrical impedanceAcoustic impedanceRadiation impedanceTransfer matrixPhysicsComputer scienceEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper proposes an acoustic model of the saxophone mouthpiece as a transfer matrix (TM). The acoustical influence of the mouthpiece is investigated, and the TM mouthpiece model is compared to previously reported mouthpiece representations, including cylindrical and lumped models. A finite element mouthpiece model is first developed, from which the TM model is derived, and both models are validated by input impedance measurements. The comparison of acoustic properties among different mouthpiece models shows that the TM mouthpiece is more accurate than the other two models, especially in preserving the high-frequency acoustic characteristics. The TM model also produces the best overall tuning of the first several impedance peaks when coupled to a measured saxophone impedance. The internal and radiated sound pressure are synthesized for an alto saxophone connected to different mouthpiece models by jointly modeling the input impedance and the radiation transfer function using recursive parallel filters. Differences are found among mouthpiece models in terms of oscillation thresholds, playing frequencies, spectral centroids, pressure waveforms, and bifurcation delays, which can be partially explained by differences in the tuning and high-frequency characteristics.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

Explore more

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