Virtual acoustic environments for music performance, rehearsal, and recording
Bibliographic record
Abstract
A comprehensive 24-channel sound field rendering system was built at McGill University's CIRMMT Centre to immerse scholar/performer Tom Beghin in virtual concert spaces where Haydn composed and performed his keyboard sonatas, or expected them to be performed. The virtual rooms were reconstructed from detailed measurements made in Haydn's historical rooms in Europe using a high-resolution 24 bit/96 kHz impulse-response measuring system. In an acoustically treated laboratory, 24 custom loudspeaker arrays were arranged on a surface of a hemisphere surrounding the performer. Multiple fast DSP engines convolved live signals with 24 impulse responses allowing the performer to rehearse and record in each room with low-latency virtual acoustics. This paper describes the details of system design, the method of measurement, and discusses various aspects of recording and performing in virtual acoustic environments. The method has also been tested in a large room during public performance by having virtual-room responses and high-definition photo images projected from the stage. Surround sound recordings of Haydn's complete solo keyboard music made in several virtual rooms and matched to specific historical instruments will be released in a collection of 13 commercial SA-CD's by the end of 2008.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".