A glimpse of 3D acoustics for immersive communication
Bibliographic record
Abstract
Future multimedia communications systems are expected to be increasingly immersive, driven by new technology for improving 3D visual and acoustic experiences. In a classical communications sense this seems a step backwards because a much larger bandwidth is required, but the return is a richer communications mode. Acoustic technology for the audio layer is presented here, although it is not entirely inseparable from the visual aspects. The talkers' audio signals are sensed by an array of microphones assisted by a video-based talker-location system. The data is transmitted to the remote listener's location, where the 3D audio is reproduced around the listeners' ears by an array of speakers in the room. The idea is that the listener experiences being in the same room as the talkers, but achieving this convincingly has raised challenges which require new research. As a step in this direction, we use simulation in a reverberant environment to investigate the improvement from using prescribed-directivity loudspeakers for the sound field reproduction. The strong performance improvement compared to using an array of conventional omni-directional loudspeakers contributes motivation for the development of such directional loudspeakers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".