MétaCan
Menu
Back to cohort
Record W4389228202 · doi:10.3397/in_2023_0495

A precision sound pressure level measurement system

2023· article· en· W4389228202 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
KeywordsTraceabilityNoise (video)System of measurementMeasurement uncertaintyMetrologySound pressureComputer scienceAccuracy and precisionNoise measurementDigitizationAcousticsSystems engineeringEngineeringTelecommunicationsNoise reductionArtificial intelligenceStatisticsPhysicsMathematics

Abstract

fetched live from OpenAlex

Noise pollution affects human and wildlife. It is in the public interest to reduce noise levels. Measures to be taken to reduce the noise are normally expensive and must be based on facts and reliable measurements of noise. Therefore, sound pressure level measurements are key evidence to determine whether there is a breach of noise limits. The sound pressure measurement method should comply with the metrological principles concerning validation, measurement traceability and estimation of measurement uncertainty as stated in ISO/IEC Directives, Part 2, Principles and rules for the structure and drafting of ISO and IEC document. For this reason, a precision sound pressure measurement system has been developed at the National Research Council Canada. The system utilizes the latest advances in digitization and is metrologically transparent, facilitating the evaluation of measurement uncertainties. In this paper, the design and implementation of the system based on IEC 61672-1, Electroacoustics - Sound level meters - Part 1: Specifications is described. The technical challenges, such as truncation, are discussed. Closed-form solutions are obtained for typical sinusoidal sound signals. Experiments were conducted for the validation of the system. The measurement results are presented showing that the discrepancy between theoretical calculations and measurements is only 1.6 ppm.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.072
GPT teacher head0.285
Teacher spread0.212 · 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 designNot applicable
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

Explore more

Same venueNOISE-CON proceedingsSame topicStructural Health Monitoring TechniquesFrench-language works237,207