Sound Field Diffuseness in Oblique-Shaped Reverberation Rooms with Different Test Configurations
Bibliographic record
Abstract
The reverberation-room method, which assumes a diffuse sound field, has long been used for various standardized room-acoustical determinations – e.g. of surface-absorption coefficients, power levels of sound sources, transmission losses of acoustical partitions, etc. However, despite the existence of a number of standards, those outline guidelines to conduct reverberation-room measurement, the accuracy of the method is still questionable, which could be attribute to the insufficient field diffuseness. To investigate the problem and propose solutions, an oblique-shaped reverberation room with the shortest vertical dimension and ISO prescribed size of 150 m3 is considered for six different configurations: empty, with diffusers, with absorbent corner treatment, with diffuse surface reflection, with diffusers and absorbent corner treatment and with diffusers and diffuse surface reflection. A numerical finite-element-based modal approach has been utilized, and a number of room acoustical parameters have been used as descriptors to quantify the degree of sound field diffuseness. Analyzing the results, it has been found that the rooms with diffusers and absorbent corner treatments yield improved sound-field diffuseness, hence better prediction accuracy, while the rooms with diffuse surface reflection yield poor field diffuseness due to the increased surface absorption.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".