Evaluating packet erasure recovery techniques for audio streaming
Bibliographic record
Abstract
Perceived audio quality is an important metric in determining resilience of voice coding techniques against packet loss when streaming audio over Internet Protocol (IP) networks. For highly compressed audio, relatively small packet loss can dramatically reduce the quality of the reconstructed sound. A key strategy for decreasing the impact of packet loss on audio quality is the deployment of packet erasure coding which proactively sends the redundant packets used at the receiver to recover in part from lost data. In this paper, we present a testbed for voice quality monitoring with practical audio streaming applications. The paper provides architectural insights into the design and testing of erasure codes when combined with audio streaming applications. By adjusting packetization method and coding rates that maximize resistance to packet losses, we demonstrate that good voice quality can be obtained for a wide range of the raw packet loss rates in the IP networks for a variety of voice codes like G.711 and G.729.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".