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Record W4389241529 · doi:10.3397/in_2023_0456

Determination of sound-field diffusion indices based on FMBEM incidence directivity analysis

2023· article· en· W4389241529 on OpenAlexaff
Ryo Hagiwara, Tetsuya Sakuma, Yosuke Yasuda, Takayuki Masumoto

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsDirectivityIsotropyDiffusionAcousticsMultipole expansionPhysicsField (mathematics)Mathematical analysisComputational physicsMathematicsOpticsEngineeringTelecommunicationsAntenna (radio)

Abstract

fetched live from OpenAlex

Regarding the diffuseness of sound field, several indices and their measurement methods have been proposed in terms of isotropy of traveling waves. In this paper, we attempt to calculate two kinds of diffusion indices based on the incidence directivity analysis employing the fast multipole boundary element method (FMBEM). The directivity analysis is performed for steady-state sound fields in a rectangular room with changing the absorption condition of the walls. As for diffusion indices, the directional diffusion coefficient is determined from the deviation of directivity of sound intensity, while alternative isotropy indicator is calculated through the spherical expansion of the directivity. The numerical results show general tendencies that the former index is almost constant, whereas the latter index decreases with increasing the frequency. As a common tendency, it is confirmed that the indices become higher in a reflective room condition, and remarkable dips occur around the natural frequencies when absorbing walls are unevenly distributed. Moreover, concerning the receiving cell, the size of it has little effect on the indices. On the other hand, it is verified that the indices become higher with the receiving cell near the point source.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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