Fast System Identification Using Affine Projection and a Critically Sampled Subband Adaptive Filter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Classical least-mean-square (LMS) adaptive filtering algorithms for system identification are popular and conceptually simple. In many applications subband adaptive filter structures have been shown to be superior computationally and performance-wise. This paper presents a novel subband affine projection algorithm (APA) suitable for use within a recently proposed adaptive filter structure employing critically sampled filter banks. The algorithm is described in the context of measuring a room impulse response for acoustic echo cancellation in hands-free telephony. Experimental results with speech input signals in a conference room show that a four-channel subband adaptive filter with subband APA can achieve an average 5 dB lower mean square error than a subband normalized LMS
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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