Sound Localization on Tabletop Computers
Bibliographic record
Abstract
Tabletop computers (also known as surface computers and smart tables) have been growing in popularity for the past decade and are poised to make inroads into the consumer market, opening up a new market for the games industry. But before tabletop computers become widely accepted, there are many questions with respect to sound production and reception for these devices that need to be explored, particularly when it comes to multimedia consumption on the devices. For example, which loudspeaker setups should be used to take into consideration the multi-user nature of tabletop computers, and which panning method(s) maximize the spatial localization abilities of the user(s)? Previous work suggests that a quadraphonic diamond-shaped loudspeaker configuration—whereby a loudspeaker is placed at each of the four sides of the tabletop computer—leads to more accurate localization results when compared with a traditional quadraphonic loudspeaker configuration—whereby a loudspeaker is placed at each of the four corners of the tabletop computer. Given this preference for a diamond loudspeaker configuration, we examine two amplitude-panning methods (bilinear interpolation and inverse distance) for spatializing a sound on the (horizontal) surface of the table-computer with a diamond loudspeaker configuration. Results from the study detailed in this paper indicate that there are no significant differences between the two methods and that both methods are prone to error.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".