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Record W4389240832 · doi:10.3397/in_2023_0494

On the test method for short-term level fluctuation of sound calibrators

2023· article· en· W4389240832 on OpenAlexaffabout
Lixue Wu, Triantafillos Koukoulas

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSound level meterNoise (video)Distortion (music)Sound pressureSound (geography)Term (time)AcousticsEngineeringAccuracy and precisionMetreTest (biology)Computer scienceNoise levelElectronic engineeringPhysicsMathematicsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Most noise bylaws state that a precision sound level meter which meets the International Electrotechnical Commission Publication 651 or the American National Standards Institute S1.4-1983 needs to be used for noise measurements. To ensure measurement accuracy, sound calibrators are used to check the sound level meter accuracy before and after any such measurements, as required by most noise regulations. The sound calibrator itself needs to be calibrated routinely and traceable to national standards. The International Standard IEC 60942, Electroacoustics - Sound calibrators, specifies performance requirements for the sound pressure level, short-term level fluctuation, frequency, and distortion and associated test methods. However, the test method for short-term level fluctuation specified in IEC 60942 is problematic. The short-term level fluctuation cannot be measured in some cases using the method specified in IEC 60942. In this paper, a detailed theoretical analysis is presented addressing this particular issue. Closed-form solutions are obtained for typical sinusoidal sound signals. Two experimental setups and measurement results for the validation of the theoretical analysis are also presented. One uses a commercial sound level meter and the other uses a precision sound pressure measurement system developed recently at the National Research Council Canada.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.068
GPT teacher head0.335
Teacher spread0.268 · 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 designBench or experimental
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 routes2
Has abstractyes

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