PERFORMANCE OPTIMISATION OF SMALL REVERBERANT ROOM WITH HANGING DIFFUSERS
Bibliographic record
Abstract
A small reverberant room can be an efficient and economic tool to provide fast diffuse field sound absorption measurements of homogeneous sound absorbers or more complex structures, and useful for developing, testing, or evaluating specification requirements in many industrial fields.However, small reverberant rooms are known to have some diffusivity issues in lower and middle frequencies, leading to unsuitable levels of consistency, reliability, and repeatability.This paper presents the results of a study made to improve the measurement performance of a 5.7-m³ reverberant room under its Schroeder frequency (around 1275 Hz) by adding hanging diffusers.Using Ray-Tracing method, a numerical parametric study was done to estimate sound absorption of various samples by varying the number, the position, and the orientation of these diffusers based on ASTM C423 and E795 standards.Moreover, extra simulation has been performed to evaluate the effect of sample size on sound absorption consistency in function of frequency.Following these prescriptions, an experimental study was done to confirm these improvements on frequency dependent sound absorption and single rating numbers such as NRC (Noise Reduction Coefficient) and SAA (Sound Absorption Average).The results show that for this room, low frequency performance is significantly increased by using 4 well-placed hanging diffusers and a sufficient sample size.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".