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Record W1989866124 · doi:10.1121/1.1428264

Effect of noise and occupancy on optimal reverberation times for speech intelligibility in classrooms

2002· article· en· W1989866124 on OpenAlexaff
Murray Hodgson, Eva‐Marie Nosal

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReverberationAcousticsIntelligibility (philosophy)Noise (video)Background noiseComputer scienceArchitectural acousticsSpeech recognitionPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The question of what is the optimal reverberation time for speech intelligibility in an occupied classroom has been studied recently in two different ways, with contradictory results. Experiments have been performed under various conditions of speech-signal to background-noise level difference and reverberation time, finding an optimal reverberation time of zero. Theoretical predictions of appropriate speech-intelligibility metrics, based on diffuse-field theory, found nonzero optimal reverberation times. These two contradictory results are explained by the different ways in which the two methods account for background noise, both of which are unrealistic. To obtain more realistic and accurate predictions, noise sources inside the classroom are considered. A more realistic treatment of noise is incorporated into diffuse-field theory by considering both speech and noise sources and the effects of reverberation on their steady-state levels. The model shows that the optimal reverberation time is zero when the speech source is closer to the listener than the noise source, and nonzero when the noise source is closer than the speech source. Diffuse-field theory is used to determine optimal reverberation times in unoccupied classrooms given optimal values for the occupied classroom. Resulting times can be as high as several seconds in large classrooms; in some cases, optimal values are unachievable, because the occupants contribute too much absorption.

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)0.002
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 score0.621
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.020
GPT teacher head0.299
Teacher spread0.279 · 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

Citations106
Published2002
Admission routes1
Has abstractyes

Explore more

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