Évolution de Certains Réseaux de Microphones dans la Reproduction de Champ Sonore. Application à l'Intérieur de Deux Théâtres Historiques
Bibliographic record
Abstract
Performing arts spaces characterize a specific group of buildings always subject to intense studies under architectural other than acoustics point of view. In Italy, the acoustics of historical theatres, as Baroque Opera houses and Renaissance concert halls, is nowadays considered the most important physical aspect of the architectural heritage. This paper analyzes the acoustical characteristics of some important Italian theatres. In order to precisely compare the analogies of the selected theatres, only the architecture realized during the 18th century has been considered. The acoustics features have been obtained by using a pre-equalized omnidirectional sound source, which emitted an exponential sound signal (ESS) acquired by a dummy head and a B-Format microphone. This technique satisfies the standard requirements of the ISO 3382:2009 and has been compared with the innovative process realized by using a 32-channel individually controlled microphone (i.e. em32 Eigenmike®). This new generation of the microphone is able to create a 3D auralization by synthesizing any real-time variable directivity pattern. In this way, both graphical analysis and 3D sound playback can be the resulting methods of how to show the room impulse response (RIR) data measured inside any performing arts space.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".