A tunable beamformer for robust superdirective beamforming
Bibliographic record
Abstract
Conventional superdirective beamforming is a well-known multi-microphone enhancement method with superior directivity factor (DF). However, it suffers from an inferior white noise gain (WNG), which is expressed by high sensitivity to uncorrelated noise and array inaccuracies. In this work, a beamformer with tunable superdirective gain is introduced. The proposed approach plays a role of a regularized superdirective beamformer, where instead of constraining the WNG, we minimize both white noise and diffuse noise energy in the optimization problem. In addition, by using a tunable regularization parameter, we control the amount of the beamformer DF and WNG. This single boresight solution is then extended to a multiple linear constraint beamformer, with any user-determined spatial or frequency constraints. The beamformer gain response simulations exhibit a robust and controllable solution with an efficient DF/WNG tradeoff.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".