GPU-Based One-Dimensional Convolution for Real-Time Spatial Sound Generation
Bibliographic record
Abstract
Incorporating spatialized (3D) sound cues in dynamic and interactive videogames and immersive virtual environment applications is beneficial for a number of reasons, ultimately leading to an increase in presence and immersion. Despite the benefits of spatial sound cues, they are often overlooked in videogames and virtual environments where typically, emphasis is placed on the visual cues. Fundamental to the generation of spatial sound is the one-dimensional convolution operation which is computationally expensive, not lending itself to such real-time, dynamic applications. Driven by the gaming industry and the great emphasis placed on the visual sense, consumer computer graphics hardware, and the graphics processing unit (GPU) 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. Given the widespread use and availability of computer graphics hardware and the similarities that exist between the fields of spatial audio and image synthesis, here we describe the development of a GPU-based, one-dimensional convolution algorithm whose efficiency is superior to the conventional CPU-based convolution method. The primary purpose of the developed GPU-based convolution method is the computationally efficient generation of real-time spatial audio for dynamic and interactive videogames and virtual environments.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".