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Record W4389228378 · doi:10.3397/in_2023_0902

Predicting impulse prominence and tone audibility at remote assessment locations

2023· article· en· W4389228378 on OpenAlexaff
M TORJUSSEN, Patrick Hoyle, Jo Webb, Antonio J Torija-Martinez, David Waddington

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsActive listeningJudgementImpulse (physics)Computer scienceTone (literature)Sound pressureTonalityAcousticsTelecommunicationsPsychology

Abstract

fetched live from OpenAlex

This paper studies the prediction of impulse prominence and tone audibility at remote assessment locations, with a focus on industrial and commercial sound. The British Standard 4142 provides objective methods for determining graded corrections to the equivalent continuous sound pressure level for impulsivity and tonality, but these methods require specific sound sources to be installed and operating. In planning applications, where the sources are proposed or the propagation path is intended to change significantly, the professional judgement of the acoustics practitioner is relied upon to determine the likely prominence/audibility of these features at a remote assessment location. To address this issue, online listening tests utilising simplified auralisation ('auralisation-lite') were conducted among members of the Association of Noise Consultants in the UK. The initial results suggest that objective evaluation can indeed offer an improvement over existing subjective evaluations. The work provides a new method for practitioners to predict tonal and impulsivity corrections for non-existent sources. This study also provides a comprehensive review of relevant literature and standards, including ISO 9613-2:1996 and ISO/TR 17534-3:2015. The findings have practical implications for environmental agencies, industrial settings, and other fields where accurate prediction of sound level impacts are crucial.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.039
GPT teacher head0.417
Teacher spread0.377 · 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 designObservational
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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