Ambisonics Sound Source Localization With Varying Amount of Visual Information in Virtual Reality
Bibliographic record
Abstract
To reproduce realistic audio-visual scenarios in the laboratory, Ambisonics is often used to reproduce a sound field over loudspeakers and virtual reality (VR) glasses are used to present visual information. Both technologies have been shown to be suitable for research. However, the combination of both technologies, Ambisonics and VR glasses, might affect the spatial cues for auditory localization and thus, the localization percept. Here, we investigated how VR glasses affect the localization of virtual sound sources on the horizontal plane produced using either 1st-, 3rd-, 5th- or 11th-order Ambisonics with and without visual information. Results showed that with 1st-order Ambisonics the localization error is larger than with the higher orders, while the differences across the higher orders were small. The physical presence of the VR glasses without visual information increased the perceived lateralization of the auditory stimuli by on average about 2°, especially in the right hemisphere. Presenting visual information about the environment and potential sound sources did reduce this HMD-induced shift, however it could not fully compensate for it. While the localization performance itself was affected by the Ambisonics order, there was no interaction between the Ambisonics order and the effect of the HMD. Thus, the presence of VR glasses can alter acoustic localization when using Ambisonics sound reproduction, but visual information can compensate for most of the effects. As such, most use cases for VR will be unaffected by these shifts in the perceived location of the auditory stimuli.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".