Compensation of Loudspeaker Nonlinearity in Acoustic Echo Cancellation Using Raised-Cosine Function
Bibliographic record
Abstract
The nonlinearity of a power amplifier or loudspeaker in a large-signal situation gives rise to a nonlinear distortion of acoustic signal. A conventional acoustic echo canceller using linear adaptive filters is not able to eliminate the nonlinear echo component. In this brief, a novel nonlinear echo cancellation technique is presented by using a nonlinear transformation in conjunction with a conventional linear adaptive filter. The nonlinear transformation is derived from a raised-cosine function and is exploited to compensate for the nonlinearity of a loudspeaker. The transformation parameters are updated using the normalized least mean square algorithm according to the unknown nonlinear characteristic of the loudspeaker. Computer simulations show that the proposed method yields, in general, a satisfactory cancellation performance while having a very low computational complexity
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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".