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Record W2897409004 · doi:10.1121/1.5067999

Measuring with noise? We can do better!

2018· article· en· W2897409004 on OpenAlexaff
Markus Müller-Trapet, Christoph Hoeller

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsReverberationAcousticsNoise (video)Computer scienceSound pressureTransmission (telecommunications)GaussianTransmission lossTelecommunicationsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

It has long been established that correlation-based measurement techniques, using, e.g., maximum length sequences and swept sines, outperform classical methods based on random Gaussian noise. Among the advantages of these modern techniques are better rejection of background noise and reduced measurement duration to achieve similar or better precision. These advantages are especially interesting in room and building acoustics, where many measurement positions typically have to be covered and background noise is often an issue, especially when measuring sound transmission loss or velocity level differences. Unfortunately, there is currently no provision in the ASTM standards on room and building acoustics that would allow the use of these modern measurement methods. To demonstrate the advantages of these methods, this contribution will present an example of measurements of the apparent sound transmission loss, i.e., measurements of sound pressure level differences and reverberation times. In addition to the standardized measurements according to ASTM E336, all measurements were repeated with maximum length sequences and swept sines. The results will be compared and the advantages of the modern techniques will be highlighted.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.253
Teacher spread0.235 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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