LPC quantization requirements for the GPP-CELP coder
Bibliographic record
Abstract
Code-excited linear prediction coding with generalized pitch prediction (GPP-CELP) requires linear prediction filtering of the stochastic codebook output prior to addition of the adaptive codebook (ACE) component. The ACE component represents a sequence of past reconstructed samples passed through a low-pass filter to reflect the reduced pitch periodicity of the higher speech frequencies. The spectrum of the residual manifests broad peaks leading to significantly narrower distributions in the LPC parameter space. Additionally, the quantization error of the residual may be masked by the significantly greater energy of the ACE component. This work compares the quantization requirements for the information required to represent the time-varying LPC filter of the GPP-CELP coder with that of the classical CELP coder. With non-predictive coding of the LPC information a bit-rate reduction from 20 bits/20 ms to 16 bits/20 ms appears feasible without introducing noticeable degradation due to quantization.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".