Speech and image signal compression with wavelets
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 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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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