Eyes ’n ears: A system of attentive teleconferencing
Bibliographic record
Abstract
Various teleconferencing systems exist, including systems intended for multiple speakers where a speaker must be localized. Although many sound-localization systems are available, not only are they expensive and nonportable, most require extensive audio arrays resulting in substantial computational processing. A simple, economical, and compact method of sound localization for use in a teleconferencing system is being investigated. In order to localize a sound source, this system relies solely on ITD measurements between microphone pairs, which allows the sound source to be determined as one of many possible locations on the surface of a cone. By taking the intersection of a sufficient number of cones, the location of a sound source in three-dimensional space may be determined. Our goal is to overcome the problems associated with the sound-localization systems currently available. In particular, this work seeks to develop an affordable, limited maintenance and compact sound-localization system for use with a teleconferencing system capable of locating and tracking a speaker in a multiple speaker setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".