Simulating the acoustics of classical sound recording techniques in rooms
Bibliographic record
Abstract
Recently, a method to position sound sources in 3-D space using virtual microphone control has been proposed [J. Acoust. Soc. Am. 117, 2391]. In this computer-generated environment, gains and delays between a virtual sound source and virtual microphones are calculated according to their distances and the axis orientations of the microphone directivity patterns. In the follow-up study reported here, it was investigated how to best simulate the influence of a room on the virtual microphone recording. For this purpose, a virtual rectangular room was created using the mirror-image technique (up to second-order reflections). The room dimensions were copied from an existing concert space at McGill University. Late reverb was created using a multiple feedback delay network with a time-variant architecture to enable modulation. To evaluate the system, measured impulse responses between a sound source and a five-channel microphone setup were compared to the virtual impulse responses for the same room/microphone-placement configuration. Among the tested parameters was the signal ratio between direct sound source and reflections as a function of the microphone placement and choice of microphone directivity patterns. The coherence between the simulated microphone signals was adjusted as well from the measured data. [Work supported by NSERC and VRQ.]
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".