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Record W1998385503 · doi:10.1121/1.4786473

Measurements of sound leaks or ‘‘hot spots’’ and their effect on the architectural speech security of closed rooms

2006· article· en· W1998385503 on OpenAlexaff
Bradford N. Gover, John S. Bradley

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMicrophoneAcousticsReverberationComputer scienceSound pressureBeamformingImpulse responseImpulse (physics)Background noiseTelecommunicationsPhysicsMathematics

Abstract

fetched live from OpenAlex

A new measurement procedure has been developed that accurately indicates the degree of speech security (speech privacy) of a closed office or meeting room. The procedure first determines the attenuation from an average sound-pressure field within a source room to single receiving points outside the room. These average-to-spot attenuations are used to predict transmitted speech levels outside of meeting rooms, which along with the background noise levels are used to derive a reliable indicator of the audibility or intelligibility of speech at the receiving points. A key aspect of the approach is that a localized weak spot in an otherwise highly insulating partition can be identified and assessed. Investigations were carried out as to the severity and detectability of different types of sound leaks intentionally introduced into an otherwise ‘‘good’’ wall separating two reverberation chambers. Steady-state and impulse response measurements of received pressure were made at a distance of 0.25 m from the wall. In addition, a highly directional beamforming microphone array was used in an effort to quickly locate the position of potential leaks. An overview of the measurement procedures will be presented, including discussion of the severity of several types of ‘‘hot spots,’’ such as holes, penetrations, and electrical boxes.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.248
Teacher spread0.228 · 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 designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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