Iterative Wiener Filter Using a Kronecker Product Decomposition and the Coordinate Descent Method
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Bibliographic record
Abstract
In this paper, we present an iterative Wiener filter suitable for the identification of long-length impulse responses that own the low-rank feature. Many real-world systems have these characteristics, e.g., like the network and acoustic echo paths. The proposed algorithm relies on the nearest Kronecker product decomposition of the impulse response and reformulates the original system identification problem (that involves a single long-length filter) into a combination of two much shorter filters. Besides, we use the coordinate descent method to solve the two resulting normal equations. Simulations performed in the context of echo cancellation indicate that the proposed iterative Wiener filter achieves good performance, especially in challenging cases, e.g., less accurate statistics’ estimates and noisy conditions.
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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