Recent Experiences With Electronic Acoustic Enhancement In Concert Halls And Opera Houses
Bibliographic record
Abstract
This paper gives a brief summary of acoustical theory based on human perception. It then uses this theory to discuss the design and performance data of electronic acoustic enhancement systems installed in a number of opera houses and concert halls. The installations include the Deutches Staatsoper in Berlin, the Hummingbird Center in Toronto, and the Adelaide Festival Center Theater in Adelaide, Australia. Solutions to the problems of maintaining optimum clarity of the singers while providing optimum envelopment for the orchestra are given. Mrbisch INTRODUCTION Electronic acoustic enhancement of spaces for music performance has frequently been long on promise and short on performance. The major problem has been uncontrolled acoustic feedback between the microphones and the loudspeakers in the enhancement system. This feedback induces an artificial metallic coloration into the system. Avoiding the coloration has meant operating the system at loop gains that provide little overall ben...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".