Standards for multi-stream and multi-device media synchronization
Bibliographic record
Abstract
Media synchronization is getting renewed attention with ecosystems of connected devices enabling novel media consumption paradigms. Social TV, hybrid TV, and companion screens are examples that are enabling people to consume multiple media streams at multiple devices together. These novel use cases require media synchronization, as unfortunately there are substantial delay differences between the various delivery routes for television and streaming media. Broadcasters have started using proprietary solutions for over-the-top media synchronization, such as media fingerprinting or media watermarking technologies. Given the commercial interest in media synchronization and the disadvantages of proprietary technologies, consumer-equipment manufacturers, broadcasters, and telecom and cable operators have started developing a new wave of international standards for media synchronization. This article provides an overview of recently published standards from the most relevant bodies: IETF, ETSI, MPEG, DVB, HbbTV, and W3C.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.013 |
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".