An acoustic echo cancellation scheme using raised-cosine function for nonlinear compensation
Bibliographic record
Abstract
The nonlinear distortion of an acoustic signal caused by the nonlinearity of a power amplifier and/or loudspeaker may give rise to a nonlinear component in acoustic echo cancellation systems. A conventional acoustic echo canceller (AEC) using linear adaptive filtering is not able to eliminate the nonlinear echo component to a satisfactory degree. In this paper, we present a nonlinear echo cancellation technique that uses a nonlinear transformation along with a regular linear adaptive filter for the compensation of the nonlinear echo component. A raised-cosine function is used to derive the nonlinear transformation and the parameters of the nonlinear compensator are updated to adapt to the nonlinearity of the unknown distorting path. The proposed method is simulated in conjunction with the conventional normalized least mean squared algorithm, showing a superior performance of the proposed acoustic echo canceller.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".