Acoustic rendering of a virtual environment based on virtual microphone control and binaural room scanning
Bibliographic record
Abstract
Binaural room scanning (BRS) is a convolution technique that utilizes a set of spatially indexed impulse responses that are selected in response to listener head movements during virtual acoustic rendering, such that the direction of sonic elements is automatically updated according to the angle determined by a head tracker. Since the room impulse responses have to be measured for only a few loudspeaker positions, a very successful application of BRS is the simulation of a control room. In order to create a flexible headphone-based virtual environment, it is proposed to simulate the input signals for the BRS system using virtual microphone control (ViMiC). ViMiC renders a virtual environment by simulating the output signals of virtual microphones that can be used to address a surround loudspeaker system. The advantages of this approach are twofold. First, the measured impulse responses of the BRS system ensure high spatial density in overall reflection patterns, avoiding the typical gaps in between the early reflections that occur when using solely mirror images or ray tracing. Second, the resulting imaging is closer to common audio engineering practice, where a pleasant but plausible environment is favored over a more purely realistic rendering.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".