A panoramic video and acoustic beamforming sensor for videoconferencing
Bibliographic record
Abstract
Videoconferencing systems in use today typically rely on either fixed or pan/tilt/zoom cameras for image acquisition, and close-talking microphones for good quality audio capture. These sensors are unsuitable for scenarios involving multiple users seated at a meeting table, or non-stationary users. In these situations, the focus of attention should change from one talker to the next, and if possible track moving users. This work describes a multi-modal perception system using both video and audio signals for such a videoconferencing system. An omnidirectional video camera and an audio beamforming array are combined into a device placed in the center of a meeting table. The video and audio is processed to determine the direction of who is talking, a virtual perspective view and directional audio beam is then created. Computer vision algorithms are used to find people by motion and by face and marker detection. The audio beamformer merges the signals from a circular array of microphones to provide audio power measurements in different directions simultaneously. The video and audio cues are combined to make a decision as to the location of the talker. The system has been integrated with OpenH.323 and serves as a node using Microsoft NetMeeting.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".