A Robust Decomposition-Based RLS Algorithm for Echo Cancellation Applications
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Bibliographic record
Abstract
Echo cancellation is one of the most popular applications of adaptive filtering algorithms. In this framework, the algorithms have to be equipped with fast convergence/tracking features while should also be robust to different background perturbations. In terms of the convergence criteria, the decomposition-based recursive least-squares (RLS) algorithm represents a very appealing choice. It exploits an impulse response decomposition that relies on low-rank approximations and combines the estimates provided by two shorter adaptive filters using the nearest Kronecker product (NKP). In this paper, we develop a regularized version of the RLS-NKP algorithm with improved robustness features. The regularization components incorporate specific terms related to the background perturbations and model uncertainties, which are evaluated in a simple yet practical manner. Simulation results obtained in the context of network and acoustic echo cancellation support the performance gain.
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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 | 0.001 |
| 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