4800 Bps RELP Vocoder using vector quantization for both filter and residual representations
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
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 i <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> which 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\Log_{2}N</tex> dot 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 I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> which 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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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 | 0.001 |
| 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