Interactive and aliasing-free acoustic modeling of reflections and diffractions in architectural environments
Bibliographic record
Abstract
A primary challenge in geometrical acoustic modeling is computation of reverberation paths from sound sources fast enough for real-time auralization. This paper describes an aliasing-free beam tracing algorithm based on precomputed spatial subdivision and beam tree data structures that enables real-time acoustic modeling and auralization for sound sources in interactive applications. The proposed method traces convex polyhedral beams from the location of each sound source and receiver through a precomputed spatial subdivision data structure, constructing a beam tree representing the regions of space reachable by potential sequences of transmissions, diffractions, and specular reflections at surfaces of a 3D polygonal model. By computing beam trees asynchronously (off-line), our system can generate reverberation paths between sources and receivers at interactive rates and spatialize audio signals in real-time as sources and receivers move under interactive user control. Unlike previous geometrical acoustic modeling work, our beam tracing method: (1) supports evaluation of early reverberated paths at interactive rates, (2) scales well for large, densely occluded architectural environments, and (3) computes paths of diffraction without aliasing using the uniform theory of diffraction. This system is being used to develop interactive applications in which a user experiences a virtual environment immersively via simultaneous auralization and visualization.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".