Nonlinear adaptive filtering for echo cancellation
Why this work is in the frame
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Bibliographic record
Abstract
An examination is made of the problem of nonlinear adaptive filtering for echo cancellation. The high-speed requirements of digital subscriber loops and voiceband data modems place constraints on the design of adaptive echo cancellers due to the presence of nonlinearities. The authors consider table look-up structures and nonlinear filters based on the Volterra series for general nonlinearities, and nonlinear compensators for specific practical configurations. For the first category, means to speed up the initial convergence of an adaptive table look-up structure are suggested. The configurations involve two table-driven structures, one for cancellation and one to form the reference signal. It is shown how the second structure can also be used for decision-feedback equalization. A combined linear and nonlinear structure with improved convergence behavior is also proposed. Theoretical convergence rate results are presented for such structures. In the second category, nonlinear compensators are cascaded with linear filters to combat nonlinearities for specific channel models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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