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Record W2020524364 · doi:10.1121/1.429597

On the combined effects of early- and late-arriving sound on spatial impression in concert halls

2000· article· en· W2020524364 on OpenAlexaff
John S. Bradley, Rebecca Reich, Scott G. Norcross

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2000
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsImpressionSound (geography)AcousticsSalientField (mathematics)Computer sciencePhysicsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes six new experiments involving subjective ratings of the listener envelopment, LEV, and the apparent source width, ASW, of simulated sound fields. Previous work has identified LEV and ASW as the principal components of spatial impression in concert halls and has shown that ASW is primarily influenced by the level of early lateral reflections and LEV by late-arriving lateral reflections. The new results in this paper show that LEV can result from nonlateral late-arriving sounds and demonstrate the conflicting effects of early- and late-arriving lateral sound on ASW and LEV when both are present, as would occur in real halls. While it is possible to create simulated sound fields with only either LEV or ASW, in typical concert halls, the balance between early- and late-arriving lateral sound will determine the relative importance of LEV and ASW. LEV and ASW are shown to be perceived when the critical components of the sound field are salient relative to other components. The results of the new subjective studies were used to estimate expected ASW and LEV in 16 halls. In these halls LEV is predicted to be the stronger component of spatial impression.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicHearing Loss and RehabilitationFrench-language works237,207