Coding of speech signals using fractal prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years several papers on nonlinear prediction applied to speech coding have shown that these techniques can obtain better performance than traditional linear prediction. In this article we present how fractal prediction, a nonlinear technique, can be successfully used in speech coding. First we describe the basic iterated function system theory on which fractal prediction is based. We then introduce those changes necessary to obtain an algorithm that can be used in speech coding. The performance of this coding method is compared with that of the standard ADPCM coder G.726, and shows the better results of the fractal method. Finally, two perceptual criteria are introduced in the original coder to achieve higher quality and lower bit rates. The first of these methods consists in perceptually weighting the error signal before minimization, as most LPC speech coders do. The second method consist in filtering the signal before applying the fractal coder; in this scheme the filter is used to transform the signal to the so-called perceptual space. Then the output from the fractal decoder must be passed through the inverse filter to obtain the final signal. With this scheme the coder can achieve a bit rate of 16 kbps with good quality.
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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 | 0.000 | 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) | 0.000 | 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