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Record W2040292693 · doi:10.1121/1.4781689

Practical design and assessment of architectural speech privacy for closed rooms

2007· article· en· W2040292693 on OpenAlexaff
Bradford N. Gover, John S. Bradley

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsIntelligibility (philosophy)Computer scienceActive listeningAcousticsMoment (physics)Noise (video)Speech recognitionArchitectural acousticsSoundproofingReverberationArtificial intelligencePsychologyPhysics

Abstract

fetched live from OpenAlex

Practical procedures have been developed for predicting levels of speech privacy associated with closed meeting rooms. The procedures can be used to specify construction designs for rooms intended to provide desired levels of speech privacy. They can also be used for measurement, assessment, and rating of existing rooms. The new approach relates the predicted or measured sound insulation provided by the wall construction directly to the probability that speech will be audible or intelligible to bystanders outside a room. The audibility or intelligibility of speech depends on the relative levels of speech and background noise at the listeners position and can therefore be determined from the speech level inside the room, the sound insulation, and the noise level at the listening position. However, speech and noise levels vary from moment to moment. The measured statistics of these variations can be used to predict the probability that speech will be audible or intelligible outside the room. Wall constructions having higher sound insulation will result in lower probabilities of speech being overheard, and therefore correspond to higher degrees of speech privacy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.531
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.041
GPT teacher head0.365
Teacher spread0.325 · 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
GenreMethods

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
Published2007
Admission routes1
Has abstractyes

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