Eyes and ears: Attentive teleconferencing utilizing audio and video cues
Bibliographic record
Abstract
The multiple speaker teleconferencing systems currently available typically focus on a single speaker and provide limited, if any, automatic speaker tracking technologies. However, in a multiple-speaker setting, speakers must be localized and tracked in both the video and audio domains. Although many fast and portable video trackers capable of locating and tracking humans exist, they employ conventional cameras thereby providing a narrow field of view. In addition, audio localization systems are expensive, nonportable, and computationally intensive. Furthermore, there have been very few attempts to combine both audio and visual systems. This work investigates the development of a simple, economical, and compact teleconferencing system utilizing both audio and video cues. An omni-directional video sensor is used to provide a view of the entire visual hemisphere thereby providing multiple dynamic views of all participants. Using a statistical color model and simple geometrical properties, the location of each participant’s face is determined and provided to the audio system as a possible direction to a sound source. Beam forming with a small, compact microphone array allows the audio system to detect and focus on the speech of each participant. The results of experiments conducted in normal, reverberant environments indicate the effectiveness of the system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".