Predicting reverberation times in a simulated classroom
Bibliographic record
Abstract
By varying the sound-absorption treatments in a simulated classroom, experimental results were compared with analytical and computer predictions of reverberation time. Analytical predictions were made with different absorption exponents, which are the result of different weighting procedures involving room surface areas and the sound-absorption coefficients. Sound scattering was found to influence measured reverberation times. With the amount of sound scattering provided, more accurate analytical predictions were obtained with absorption exponents that give reverberation times longer than those obtained with the Sabine absorption exponent, which consistently underpredicted reverberation times. However, none of the absorption exponents could be singled out as more adequate because of similar average accuracy. Computer predictions of reverberation time were accomplished with two commercially available ray-based programs, RAYNOISE 3.0 and ODEON 2.6, with specular and calibrated diffuse reflection procedures. Neither type of procedure, in either program, was more accurate than the best analytical predictions. With RAYNOISE, neither the specular nor the calibrated diffuse reflection procedure could be singled out as more adequate. For ODEON, the calibrated diffuse reflection procedure gave consistently more accurate predictions than its specular reflection procedure, with the best accuracy of the computer predictions.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".