Implementation of a full duplex 2.4 kbps LPC vocoder on a single TMS-320 microprocessor chip
Bibliographic record
Abstract
With the commercial availability of high speed digital signal processors, it is now possible to implement all the linear predictive coding (LPC) tasks (excluding D-A/A-D conversion) on a single chip. In this paper, a very small, high quality, full-duplex, 10th order 2.4 kbps LPC vocoder is described. A single Texas Instruments TMS-320 microprocessor performs LPC analysis, pitch detection, synthesis, and data I/O. At the time of writing this paper, a total of 20 off-the-shelf integrated circuits were used occupying two thirds of a 14cm × 18cm wirewrap board (excluding power supply). The total power dissipation is less than 2 watts. The chip count may be reduced by a factor of two by combining the random logic on a semi-custom integrated circuit. When produced commercially, the cost of this vocoder should be considerably less than existing LPC units.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".