4800 Bps RELP Vocoder using vector quantization for both filter and residual representations
Bibliographic record
Abstract
The paper presents the full description and discusses the performances of a 4800 bit per second residual excited linear prediction vocoder. The LPC analysis is efficiently performed using a type of binary-tree search vector-quantization approach. The technique, which is described in ref (1), uses a set of hyperplane equations to perform a hierarchical pattern classification of the input autocorrelation vector in the autocorrelation space. The end result of the search is the integer i1which is the index of the most appropriate (in the Itakura-distance sense) prediction filter out of a set of N preset filters. The search requires only\Log_{2}Ndot products. In this case vector quantization presents two advantages over the classical approach of the Durbin algorithm followed by scalar quantization. First, a faster algorithm is obtained. Second, the same accuracy in filter representation is possible with less bits per second and consequently more bits can be allocated for representing the residual and gain. The residual is vector quantized in the time domain by blocks of 16 the samples according to the approach of ref (2). The 16 sample block is essentially encoded using the integer I2which is the index of the most appropriate 16-sample waveform out of set of M preset prototype waveforms stored in memory. The paper includes preference testings for comparison with other types of 4800 Kbit/sec vocoders. Some sample recordings will be presented at the conference. Finally, preliminary results in the attempt to implement the vocoder in real time on a MAP 200 array processor are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".