An improved CLMS algorithm for feedback cancellation in hearing aids
Bibliographic record
Abstract
In LMS algorithm-based feedback estimation, the value of the adaptation step size chosen imposes establishes a compromise between the speed at which the algorithm converges to the feedback-path estimate and the misadjustment between the true and estimated feedback paths at steady state . The combined LMS (CLMS) scheme overcomes this issue, but itself suffers from a sluggish adaptation of the mixture parameter during periods of a rapidly-varying or a stationary feedback path, leading to a degradation in the performance of the feedback canceller. In this work, we propose an acoustic feedback canceller with an improved affine combination of two different-step-size LMS filters, for a bias-less estimation of the acoustic feedback. The new filter-combiner parameter controls the filter combination and ensures at least a minimum adaptation of the mixture parameter for a stationary as well as a varying acoustic environment. We analyse the proposed algorithm for feedback reduction and prove that it performs as well as the element filters or even better in some situations, as compared to the CLMS algorithm. A detailed behaviour analysis of the proposed algorithm is also presented for scenarios of a stationary as well as a time-varying acoustic environment of the user. Simulation results verify the validity of the derived expressions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".