Sound source localization with various ambisonics orders in virtual reality
Bibliographic record
Abstract
To reproduce realistic audio-visual scenarios in the laboratory, Ambisonics is often used to present a sound field over loudspeakers and virtual reality glasses are used to present visual information. However, the application of both technologies, Ambisonics and virtual reality glasses, might affect the spatial cues for auditory localization, resulting in reduction of the localization accuracy. Furthermore, the combination of both technologies might introduce further errors. Here, we investigated how a head-mounted display affects the localization of virtual sound sources produced using either 1st, 3rd, 5th or 11th order Ambisonics with and without visual information. Preliminary results suggest that there is an effect of Ambisonics order on localization accuracy, mostly in the first order Ambisonics, but that the virtual reality glasses do not add an additional error. The localization error with first order ambisonics remained even with visual information as the perceived location matched better with other visual sources (i.e., nearby loudspeakers). Thus, virtual reality can be used in combination with Ambisonics without additional localization errors, but for localization accuracy it is better to use higher order Ambisonics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".