Adaptive filtering with decorrelation for coloured AR environments
Bibliographic record
Abstract
The aim of this paper is to improve the convergence speed and steady state error of LMS-type adaptive algorithms for coloured and nonstationary signals such as in acoustic echo cancellation. The performance of these algorithms is limited by the eigenvalue spread of the correlation matrix of the input signal and also by the power of the additive noise. In this paper, the decorrelating adaptive algorithms are classified into four types: input-decorrelating, error-decorrelating, joint-prefiltering and a combination of joint-prefiltering and input-decorrelating. The last two types of algorithms are studied and guidelines are given to choose the proper algorithms based on the power spectral densities of the input signal and noise. For a prefiltering structure, it is proven that if the adaptive filter operates on any prefiltered pair of input and desired signal the optimal solution will remain unchanged. It is suggested that a new adaptive decorrelation prefilter be included that is designed to achieve two objectives simultaneously: to increase the speed of convergence by reducing the correlation between the prefiltered samples of the input; and to improve the tracking and the steady state performance by reducing the noise power in the prefiltered domain. Simulations and theoretical results confirm that the introduced auxiliary whitening processes improve the performance of the adaptive algorithms by jointly whitening the input and the error signal.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".