L-infinity-NORM BASED PARTIAL-UPDATE ADAPTIVE FILTERING ALGORITHM FOR ECHO CANCELLATION
Bibliographic record
Abstract
We provide a framework for developing a low-complexity adaptive filtering algorithm by incorporating the concept of partial-updating into the technique of finding the gradient vector in the hyperplane based on the L/sub /spl infin//-norm criterion. The resulting algorithm is referred to as the partial-update normalized sign LMS (PU-NSLMS) algorithm. A specific case of the PU-NSLMS algorithm, called the M-Max PU-NSLMS algorithm, based on the concept of having a minimum Euclidean length of the coefficient-update vector, is considered. It is shown that this algorithm is computationally less complex compared to the partial-update normalized least-mean squares (PU-NLMS) algorithm. Results concerning the mean-square analysis of the M-Max PU-NSLMS algorithm are given. The performance of this algorithm is compared with that of the PU-NLMS algorithm in the case of network echo cancellation. It is shown that the convergence rate of the proposed algorithm is comparable to that of the PU-NLMS algorithm, but with a reduced complexity, making it a good choice for applications requiring a long filter tap, especially for real-time implementations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".