Data-Driven White Noise Gain Constrained Robust Superdirective Beamformer for Speech Enhancement
Bibliographic record
Abstract
Superdirective beamformers are highly effective at suppressing directional interference and diffuse noise, but their practical use is often constrained by the problem of white noise amplification. Robust superdirective beamforming methods typically address this by imposing a constraint on the white noise gain (WNG). However, determining the appropriate WNG threshold in varying noise environments remains unclear. This paper introduces a data-driven approach to estimating the optimal WNG threshold. Subsequently, a more versatile and robust superdirective beamformer is developed by solving a quadratic eigenvalue problem (QEP). Experimental results show that this method outperforms traditional superdirective beamformers, which rely on a WNG threshold set through a fixed search range. Importantly, this approach functions as a distortionless beamformer, maintaining high fidelity of the desired acoustic signal and allowing for additional post-filtering if required.
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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 | 0.000 | 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".