Perceptual postfilter estimation for low bit rate speech coders using Gaussian mixture models
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
A novel perceptual postfilter is introduced. For each frame, the filter gains, z, are estimated given a vector, y, of the quantized LSFs and the long-term prediction gain of the corresponding frame. The proposed perceptual postfilter is derived from an optimal MMSE estimator, i.e. the estimated gain vector is ˆz = E{z|y}. The MMSE estimator is based on the conditional pdf of z given y, which is computed from the joint pdf modelled by a GMM. The proposed perceptual postfilter improves the speech naturalness comparing with the conventional adaptive postfilter, while maintaining the property of being an “add-on ” postfilter without modification to the current encoder. 1
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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 | 0.003 |
| 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