Fast System Identification Using Affine Projection and a Critically Sampled Subband Adaptive Filter
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Bibliographic record
Abstract
This paper investigates the use of a subband affine projection (AP) algorithm for solving system identification problems using critically sampled subband adaptive filters. The subband AP is first analyzed with respect to theoretical rate of convergence and computational complexity. Simulation results for the algorithm are presented in the context of measuring a room impulse response for acoustic echo cancellation and tracking changes to the impulse response over time. In these simulations, the subband AP is compared to other subband adaptation algorithms using two-, four-, and eight-channel filter banks and to fullband adaptation algorithms. To evaluate the algorithm in a practical implementation, experimental results are presented for echo cancellation using speech input signals in a conference room. It is shown that a four-channel filter bank with subband AP can achieve an average mean square error that is 5 dB lower than a subband normalized least-mean-square algorithm during initial filter convergence
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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