Audiovisual localization of multiple speakers in a video teleconferencing setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Attending to multiple speakers in a video teleconferencing setting is a complex task. From a visual point of view, multiple speakers can occur at different locations and present radically different appearances. From an audio point of view, multiple speakers may be speaking at the same time, and background noise may make it difficult to localize sound sources without some a priori estimate of the sound source locations. This article presents a novel sensor and corresponding sensing algorithms to address the task of attending, simultaneously, to multiple speakers for video teleconferencing. A panoramic visual sensor is used to capture a 360° view of the speakers in the environment and from this view potential speakers are identified via a color histogram approach. A directional audio system based on beamforming is then used to confirm potential speakers and attend to them. Experimental evaluation of the sensor and its algorithms are presented including sample performance of the entire system in a teleconferencing setting. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13: 95–105, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10045
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it