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Record W4315647552 · doi:10.3390/en16020813

Proposal of a Simplified Tool for Early Acoustics Design Stage of Classrooms in Compliance with Speech Intelligibility Thresholds

2023· article· en· W4315647552 on OpenAlexaff
Paolo Croce, Francesco Leccese, Giacomo Salvadori, Umberto Berardi

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIntelligibility (philosophy)CLARITYComputer scienceSpeech recognition

Abstract

fetched live from OpenAlex

The speech intelligibility properties of classrooms greatly influence the learning process of students. Proper acoustics can promote the inclusion of foreign students and children with learning or hearing impairments. While awareness of the topic is increasing, there is still no parameter that can describe all aspects of speech transmission inside a room. This complicates the design of classrooms and requires designers to have extensive knowledge of theory and experience. In the scientific and technical literature, there is a lack of predictive tools, easy to use by designers, which can guide the choices in the early design stages in order to move towards technical solutions able to ensure adequate levels of speech intelligibility. For this reason, in this paper, the most relevant speech intelligibility parameters found in the literature were collected and discussed. Among these, the Clarity index and Speech Transmission Index were singled out as the most effective ones, whose prediction can be made with relatively simple methods. They were then analyzed through their prediction formulas, and a tool was proposed to allow an easy estimation of the minimum total equivalent sound absorption area needed in a classroom. This tool greatly simplifies the early acoustics design stage, allowing the intelligibility of speech within a classroom to be increased without requiring much theoretical effort on the part of the designers.

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)0.000
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 score0.460
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.123
GPT teacher head0.417
Teacher spread0.294 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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