Development of a Windows[superscript]T[superscript]M 3-D sound system using binaural technology
Bibliographic record
Abstract
It is anticipated that 3-D sound will become a major component in virtual reality environments. In this thesis, we investigate the development of a Windows(TM) 3-D sound system using binaural technology. This work should eventually be integrated in complex virtual environment software. The 3-D sound effects are achieved by applying a filter pair representing audio cues from a specific point in space to the left and right channels of headphones. As a first step, an offline 3-D audio file generation system is developed, and effects such as sound reflections in a virtual rectangular room are added to this system. The real-time implementation is considered next. The first approach tested for the real-time implementation uses the time domain convolution of a sound source signal with head-related transfer functions, but the computational load of this approach is such that it cannot run in real-time at audio sampling rates. As an alternative, a second approach with a Fast Fourier Transform overlap and save technique is used to reduce the computational load, and a real-time implementation at audio frequencies is therefore successfully achieved.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.026 |
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".