Bibliographic record
Abstract
The authors consider time-frequency multiresolution analysis based on wavelets, as it applies to speech/audio and image/video signal compression. They compare the wavelet analysis to the traditional short-window techniques used in signal compression. The performance of the discrete wavelet transform in terms of the bit rates and signal quality is comparable to that for other techniques such as the discrete cosine transform (DCT) for images and code-excited linear predictive coding (CELP) for speech, but with much less computational burden. Experiments with an image and Daubechies's four-coefficient wavelet show that truncation of wavelet coefficients as high as 90% still produces 30-dB peak signal-to-noise ratio (PSNR) quality. This is better than DCT. In an experiment on a male spoken sentence, the scheme reaches a 12.82-dB segmental signal-to-noise ratio (SEGSNR) at a rate of less than 4.8 kb/s. In comparison, the state-of-the-art CELP coding at 4.8 kbit/s can attain SEGSNR of 10-13 dB. Other experiments with images and Haar two-coefficient wavelet are also highlighted.>
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".