Pitch-synchronous linear-prediction analysis by synthesis with reduced pulse densities
Bibliographic record
Abstract
An important step toward achieving a high-quality 4 kb/s speech codec is reducing the coding-rate of the stochastic codebook component to near 2 kb/s. The increased reconstruction error in the residual that such low-rate quantization implies motivates the search for techniques that reduce the perceptibility of the errors in the reconstructed signal. Pitch-synchronous estimation of the linear-prediction filter and pitch-synchronous updating of the adaptive codebook reduce the coefficient-estimation error and increase the relative contribution of the adaptive codebook component to the synthesized signal, thereby reducing audible noise. However, pitch synchronous analysis normally results in a variable-rate coder. To obtain a fixed-rate representation, we introduce an efficient representation of the stochastic codebook component using a pulse density of one pulse per 2 ms and signed magnitudes specified by 2 bits per pulse-pair. The resulting reconstructions are evaluated for CELP coders corresponding to classical and generalized-pitch-predictor designs. In both cases speech quality comparable to 8 kb/s G.729 is achieved.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".