Ceiling baffles and reflectors for controlling lecture-room sound for speech intelligibility
Bibliographic record
Abstract
Reinforcing speech levels and controlling noise and reverberation are the ultimate acoustical goals of lecture-room design to achieve high speech intelligibility. The effects of sound absorption on these factors have opposite consequences for speech intelligibility. Here, novel ceiling baffles and reflectors were evaluated as a sound-control measure, using computer and 1/8-scale models of a lecture room with hard surfaces and excessive reverberation. Parallel ceiling baffles running front to back were investigated. They were expected to absorb reverberation incident on the ceiling from many angles, while leaving speech signals, reflecting from the ceiling to the back of the room, unaffected. Various baffle spacings and absorptions, central and side speaker positions, and receiver positions throughout the room, were considered. Reflective baffles controlled reverberation, with a minimum decrease of sound levels. Absorptive baffles reduced reverberation, but reduced speech levels significantly. Ceiling reflectors, in the form of obstacles of semicircular cross section, suspended below the ceiling, were also tested. These were either 7 m long and in parallel, front-to-back lines, or 0.8 m long and randomly distributed, with flat side up or down, and reflective or absorptive top surfaces. The long reflectors with flat side down and no absorption were somewhat effective; the other configurations were not.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".