Backward Linear Prediction for Lossless Coding of Stereo Audio
Why this work is in the frame
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Bibliographic record
Abstract
Lossless audio coding aims at achieving the lowest possible bitrate for transmission or storage of audio without any loss of information. This is usually done by first removing redundancy from the audio signal, and then applying entropy coding to the residual signal. Linear prediction (LP), when applied to monophonic signals, is a very effective way to remove redundancy. It produces minimum-phase predictors that are efficiently compressed by combining vector quantization with a meaningful representation of the LP coefficients (such as the LSFs). When applied to stereo signals however, joint channel prediction often produces non-minimum-phase predictors, whose quantization requires a high bit rate and poses stability problems. In this paper, we show that backward estimation of the LP coefficients (where those are estimated on the past decoded signal) solves most of the problems associated with the use of joint channel prediction in a lossless audio coder.
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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 | 0.001 | 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