Echo Cancellation: A Novel Adaptive Kalman Filter-Based Scheme
Bibliographic record
Abstract
A novel adaptive echo cancellation scheme, using an accurate and reliable two-stage identification scheme and an adaptive Kalman filter (KF), is proposed.The novel scheme estimates a desired waveform from the received signal which is corrupted by an undesired echo and noise.It is assumed that the desired waveform and the echo are uncorrelated with each other, and that the reference waveform is highly correlated with the echo.Further, the reference waveform and echo are related by a rational transfer function and are assumed to be measureable.An accurate and reliable model of the echo is identified from the received signal using a proposed novel two-stage identification scheme.This two-stage scheme is used to identify accurately and reliably the model relating the reference and actual echo waveforms using least squares and model order reduction.It is implemented in the frequency domain using waveform segmentation to ensure efficient computation and model reduction, signal stationarity and real-time system implementation.The identified model of the echo is then embodied into the KF which is a minimum-variance estimator that is robust to noise and disturbances and has a zero-mean, white noise residual.The performance of the KF is monitored continuously and its gain updated adaptively.If the filter's residual fails the whiteness test, the model of the echo is then re-identified and the KF adapted accordingly.The proposed scheme was successfully evaluated on simulated and real recorded speech corrupted by noise and echo.This novel scheme can be extended to areas such as beamforming
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".