Bibliographic record
Abstract
Computer generated images in cinema and games are rendered based on detailed physical models of the scene, resulting in very natural looking (realistic) images as perceived by a human observer. Sound is most often rendered with limited or no reference to these models. Thus the rendered sound does not achieve the level of realism that is potentially available by using the models. In this paper we review methods used for sound mixing and rendering for cinema and games. Acoustic models were standardized in MPEG-4, but are not used widely. Modern cinema sound rendering uses one of the new tools that are popular with cinema directors and producers that do not appear to refer to a scene model. Game sound engines do use scene models for obstructions but not reverberation. For any new method to be successful, it must yield obviously better results with reasonable CPU load and fit into the workflow. A game engine solution is to use the MPEG-4 scene models augmented by adjustable perceptual parameters and convolution with measured reverberation tails. This solution requires a tool and library to enable acoustic properties to be assigned to a visual scene and frequency dependent acoustic distribution (radiation) patterns to be assigned to sound sources.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".