Fast Recovery for a CELP-Like Speech Codec After a Frame Erasure
Bibliographic record
Abstract
The adaptive codebook used in code-excited linear prediction (CELP)-like speech codecs is very effective for modeling the quasi-periodic component of the excitation signal but, unfortunately, introduces a strong interframe dependency that renders the decoder vulnerable to frame erasures. For voiced speech, the error affects not only the erased frame but also all the subsequent frames. In this paper, a technique to improve the recovery after a frame erasure is proposed. The technique consists in a constrained excitation search at the encoder and a resynchronization procedure at the decoder. The constraint aims at reducing the contribution of the adaptive codebook by making the innovation codebook partially model the pitch excitation. Further, for highly voiced frames, the pitch-related information contained in the innovation excitation is exploited at the decoder to speed up the resynchronization of the adaptive codebook after a frame erasure. When applied to the adaptive multirate wideband (AMR-WB) codec, the method brings a significant improvement in the case of frame erasures, at the cost of a minor quality loss compared to the standard codec at the same bit rate. The method does not need additional delay and has the advantage of maintaining full interoperability between the standard codec and its modified version.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".