An efficient interleaved tree-structured almost-perfect reconstruction filterbank
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Bibliographic record
Abstract
In the conventional DFT filterbanks, the required transition bandwidth of the constituent prototype filter becomes narrower with increasing the number of subbands. This happens at the expense of making the prototype filter length longer, rendering the corresponding hardware implementation impractical beyond a certain number of subbands. This paper presents an alternative approach to the design of almost-perfect reconstruction (almost-PR) filterbanks which circumvents the problem associated with a large number of subchannels. The resulting filterbank has a multi-stage tree-structured configuration, and can be efficiently implemented by using interleaving techniques (to eliminate all identical short-length digital filters). Unlike the conventional DFT filterbanks, the resulting filterbank requires much smaller number of non-zero multiplier coefficients, and when the number of subchannels changes, the design of this tree-structured filterbank needs to be modified only slightly.
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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