Multichannel active noise control using numerically robust recursive least-squares algorithms
Bibliographic record
Abstract
Recursive least-squares (RLS) algorithms and fast-transversal-filters (FTF) algorithms were recently introduced for multichannel active noise control (ANC) systems. It was reported that these algorithms can greatly improve the convergence speed of ANC systems using adaptive FIR filters, compared to steepest descent algorithms or their variants. However, numerical instability of the algorithms was an issue that needed to be resolved. In this presentation, extensions of stable realizations of recursive least-squares algorithms such as the inverse QR-RLS and the QR decomposition least-squares-lattice (QRD-LSL) algorithms are first introduced for multichannel ANC. A first set of simulations will verify that these algorithms have indeed a better numerical stability than the previously published recursive least-squares ANC algorithms. The case of underdetermined ANC systems (i.e., systems with more actuators than error sensors) is then considered, to show that in these cases it may be required to use constrained algorithms in order to have numerical stability. Constrained least-squares algorithms for multichannel ANC systems are therefore introduced for two types of contraints: minimization of the actuator signals power and minimization of the adaptive filter coefficients squares. A second set of simulations will verify the stabilized behavior of the constrained algorithms.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".