Spatial sound for video games and virtual environments utilizing real-time GPU-based convolution
Bibliographic record
Abstract
The generation of spatial audio is computationally very demanding and therefore, accurate spatial audio is typically overlooked in games and virtual environments applications thus leading to a decrease in both performance and the user's sense of presence or immersion. Driven by the gaming industry and the great emphasis placed on the visual sense, consumer computer graphics hardware (and the graphics processing unit in particular), has greatly advanced in recent years, even outperforming the computational capacity of CPUs. This has allowed for real-time, interactive realistic graphics-based applications on typical consumer-level PCs. Despite the many similarities between the fields of spatial audio and computer graphics, computer graphics and image synthesis in particular, has advanced far beyond spatial audio given the emphasis placed on the generation of believable visual cues over other perceptual cues including auditory. Given the widespread use and availability of computer graphics hardware as well as the similarities that exist between the fields of spatial audio and image synthesis, this work investigates the application of graphics processing units for the computationally efficient generation of spatial audio for dynamic and interactive games and virtual environments. Here we present a real-time GPU-based convolution method and illustrate its superior efficiency to conventional, software-based, time-domain convolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".