Augmented stage support in ensemble performance using virtual acoustics technology
Bibliographic record
Abstract
Perceptual evaluation of electronically varied acoustic conditions has been perfomed with 15 musicians in a chamber orchestra rehearsal. Using Virtual Acoustics Technology (VAT), a room impulse response based active acoustic enhancement system, four different acoustic conditions were presented to the musicians. Condition 1 was the natural room itself without the VAT system. Condition 2 had a moderate level of VAT enhancement with approximately 10% increased EDT value from the condition 1. Condition 3 duplicated the condition 2 except for another 10% rise of EDT, and condition 4 offered enhancement utilizing multiple early reflections IRs without extending the reverb time of the space. The conditions were presented to the ensemble in random order and the loudness of all four conditions was carefully matched within 1 dB variance. Evaluation results indicated that 65% of participating musicians prefered condition 4 and their preference was highly correlated with perceptual attributes such as “feeling of stage support,” “feeling of intimacy,” and “quality of reverberation.” The objective measurements also confirmed the improvements in stage acoustics support parameters (ST1 and ST2) in condition 4.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".