Predicting speech metrics in a simulated classroom with varied sound absorption
Bibliographic record
Abstract
By systematically varying the amount of sound absorption, and the location of the sound-absorbing material in a simulated classroom, it was possible to assess the accuracy of the prediction of speech metrics in quite simple acoustical environments. Predictions of speech level, early-to-late sound ratios (C50) and speech transmission index (STI) values were obtained analytically and with two hybrid ray-based computer programs, RAYNOISE 3.0 and ODEON 4.1. The RAYNOISE predictions were accomplished with a purely specular reflection model and also with a calibrated diffuse reflection model. ODEON uses a parameter called transition order, TO, to change the reflection procedure from purely specular to diffuse for reflections that have orders higher than TO. A parametric study was conducted to determine the best transition order for the ODEON prediction of speech metrics. It was found that the analytical predictions of speech level and C50 were on average accurate to about 1 just-noticeable difference (jnd), whereas the analytical predictions of STI were on average within 2 jnd's. ODEON predictions of speech level, C50 and STI were on average within 2 jnd's. RAYNOISE predictions of C50 and STI with the specular model were on average within 2 jnd's. However, the RAYNOISE predictions of speech level, with both types of reflection models, and the RAYNOISE predictions of C50 and STI with the diffuse model had average errors greater than 2 jnd's. The effects of the sound-absorption treatments on the measured speech metric values are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".