Affine projection algorithm for oversampled subband adaptive filters
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Bibliographic record
Abstract
The performance of the normalized least mean square (NLMS) algorithm for adaptive filtering is dependent on the spectral flatness of the reference input. Thus, the standard NLMS algorithm does not perform well in over-sampled subband adaptive filters (OS-SAFs) because colored subband signals are generated even for white input signals. Thus we propose the use of the affine projection algorithm (APA) to adapt the individual subband filters in OS-SAP systems. The OS-SAF using APA for adaptation is implemented on a fast, low-resource over-sampled filterbank. Through both theoretical and experimental analyses, it is demonstrated that a low order APA will significantly improve the convergence behavior, offering a low computational complexity compared to the recursive least squares (RLS) method. We employ a recursive method of calculating the correlation matrix to further decrease the computation cost without affecting the performance.
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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