Bi-criterion optimization of non-uniform filter banks for acoustic echo cancellation
Why this work is in the frame
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Bibliographic record
Abstract
A new method for designing non-uniform filter-banks for acoustic echo cancelation is proposed. In the method, the analysis prototype filter design is framed as a bi-criterion optimization problem that minimizes the aliasing power while maximizing the bandwidth. Consequently, an optimal trade-off curve between filter bandwidth and aliasing power is obtained, which is then used for selecting the most appropriate filter. To increase the degrees of freedom during optimization, no constraints are imposed on the phase or group delay of the filters. Experimental results show that the filter bank designed using the proposed method provides (1) greater reduction of aliasing power after analysis, (2) uniform reduction of the aliasing power across the spectrum after synthesis, and (3) greater flexibility in the selection of prototype filters.
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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 | 0.000 |
| 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