Low Complexity Adaptive Post-Whitening DCT-LMS for Echo Cancellation
Bibliographic record
Abstract
In this paper, we propose a low-complexity adaptive post-whitening discrete cosine least mean square (LC-POW-DCT-LMS) algorithm.The fundamental concept introduced in this new algorithm consists of designing an adaptive post-whitening process of first order by appropriately exploiting the fact that the adaptation process of the decorrelation coefficients becomes slow when the step size parameter takes relatively small values.For a given iteration, this new post-whitening requires the computation of only one transformation of length N, 5N+2 additions and 7N+5 multiplications, whereas the corresponding postwhitening of the existing POW-DCT-LMS algorithm requires the computation of two transformations each of length N, 4N+2 additions and 7N+4 multiplications.Therefore, the LC-POW-DCT-LMS algorithm substantially reduces the arithmetic complexity.Moreover, by considering echo cancellation application, we demonstrate that the performance of this new algorithm, for both mean square error (MSE) and normalized misalignment (MSI) convergence speed as well as for the reached steady state, is comparable to that of the existing POW-DCT-LMS algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".