Spectral distance measure applied to the optimum design of DPCM coders with L predictors
Bibliographic record
Abstract
This paper explores the intermediate solutions between fixed prediction and forward adaptative prediction in ADPCM which consists of using a finite number, L, of preselected linear predictors of order M. The design problem of selecting the optimum set of predictors with respect to the overall prediction gain is formulated and an iterative procedure is described to obtain the solutions. The relative prediction-gain improvement is computed for a 3 sec. speech sample and for several values of L,M, and block size showing that\frac{1}{2}of the adaptative over fixed-prediction improvement in dB is reached with onlyL=4and 2/3 withL=8. The design problem solved by minimizing Itakura distance is shown to yield essentially identical performances. A linear discriminant property in the autocorrelation space is pointed out. Based on that property a pattern classification approach is proposed as an hardware-efficient coding algorithm.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".