Concave surfaces and acoustics of performance spaces—Part I— Hybrid ray-image analysis
Bibliographic record
Abstract
Current acoustic practices deem that concave surfaces do not provide good acoustical performance. However, old cathedrals, churches, and enclosed performance spaces with concave interiors seem to perform well. Part I of the current investigation analyzes the acoustical performance of spaces with curved surfaces. The main focus of the current investigation was to research the uniformity of the sound field produced by curved surfaces by analyzing sound pressure level distribution throughout the audience space. It studied the impact of the focal plane on the overall sound distribution within an enclosed space. To analyze the effect of curved surfaces at different frequencies, three enclosed rooms with curved surfaces were used to measure the sound pressure levels throughout an audience space: the Paul Crocker Gallery in the Ryerson Architecture Building, Toronto; St. Martin-in-the-fields Anglican Church, Toronto; and Wigmore Hall, United Kingdom. The evaluations were achieved with both experimental methods, and computer simulations using hybrid-ray-image methods. Computer simulations were validated by the initial on-site measurements in the Toronto locations. After these evaluations were performed, results showed that in these conditions, the curved surfaces had minimal negative impact as perceived by the audience. The results of the investigation will be presented in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".