Linear prediction based packet loss concealment algorithm for PCM coded speech
Why this work is in the frame
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Bibliographic record
Abstract
One of the well-known problems in real-time packetized voice applications is the degradation in voice quality due to delayed or misrouted packets. When a voice packet does not arrive at the receiver on time, the receiver needs a packet loss concealment algorithm to generate a signal instead of the missing voice segment. In this paper we describe a high performance packet loss concealment algorithm for pulse code modulation (PCM) coded speech. The algorithm extracts the residual signal of the previously received speech by linear prediction analysis, uses periodic replication to generate an approximation for the excitation signal of missing speech, and generates synthesized speech using this excitation. It also performs overlap-and-adding and scaling operations to smooth out transitions at frame boundaries. The new algorithm is compared to other algorithms by subjective quality tests, and is found to be better than the existing algorithms in some cases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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