GPU-based acoustical occlusion modeling with acoustical texture maps
Bibliographic record
Abstract
Although the direct path between a sound source and a receiver is often occluded, sound may still reach the receiver as it diffracts ("bends") around the occluding obstacle/object. Diffraction is an elementary means of sound propagation yet, despite its importance, it is often ignored in virtual reality and gaming applications altogether except perhaps for trivial environments. Given the widespread use and availability of computer graphics hardware and the graphics processing unit (GPU) in particular, GPUs have been successfully applied to other, non-graphics applications including audio processing and acoustical diffraction modeling. Here we build upon our previous work that approximates acoustical occlusion/diffraction effects in real-time utilizing the GPU. In contrast to our previous approach, the audio properties of an object are stored as a texture map and this allows the properties to vary across the surface of a model. The method is computationally efficient allowing it to be incorporated into real-time, dynamic, and interactive virtual environments and video games where the scene is arbitrarily complex.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".