Virtual acoustics in distributed performance over a closed audio network
Bibliographic record
Abstract
We explore an application of active acoustics used to produce a shared virtual environment for live musical performance. As part of a concert given in the Immersive Media Lab at McGill University, musicians and audience members were located in adjacent but acoustically isolated spaces on the same digital audio network. With computer generated effects and live instruments, the concert consisted of electroacoustic performances that utilized dynamic virtual environments produced by our Virtual Acoustic Technology (VAT) system as an improvisatory partner and a mixing device to blend the diffusion of electronic and acoustic musical sources. The performance, including its evolving virtual environment, was captured using spatial microphone techniques and distributed in real-time to the audience over an immersive loudspeaker system in an adjacent control room. Audience members were given the opportunity to visit the musicians’ performance space in order to compare the reproduction to the original environment. Overall, the blending of computer-generated and acoustic sources created a specific use case for virtual acoustics, while the immersive capture and distribution method examined an avenue for producing a real-time shared experience. Future work in this area includes audio networks with multiple virtual acoustic environments and distributions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".