Robust regularized blind system identification with application to adaptive speech dereverberation
Bibliographic record
Abstract
The normalized multichannel frequency-domain least-mean square (NMCFLMS) algorithm is a prominent method for blind identification of multichannel acoustic systems. However, the NMCFLMS algorithm relies on a constant, determined by a block of microphone signals, to define the regularization parameter. This setup makes the algorithm sensitive to variations in speech segments and noise conditions. In this paper, we propose a variable regularization parameter that incorporates key factors, such as signal-to-noise ratio, output signal power, and filter length, to enhance the robustness of the algorithm against additive noise and the non-stationary nature of speech. Additionally, we introduce a mechanism to update the regularization parameter based on the mean-squared error of the adaptive filter, improving the ability of the algorithm to track time-varying systems. The proposed variable regularization NMCFLMS algorithm is then applied to speech dereverberation using the multichannel input-output inverse theorem method. Simulation results, using room impulse responses measured in real acoustic environments, demonstrate the effectiveness of the approach in both multichannel blind identification and speech dereverberation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".