Combined Beamformers for Robust Broadband Regularized Superdirective Beamforming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Superdirective fixed beamformers are known to attain high directivity factors, but are extremely sensitive to uncorrelated noise and slight errors in the array elements, which are modeled by the beamformer white noise gain measure. The delay-and-sum beamformer, on the other hand, manages to maximize the white noise gain, but suffers from a very low directivity factor. In this paper, we discuss the design of a broadband beamformer which controls both the directivity factor and the white noise gain. We combine a regularized version of the superdirective beamformer together with the delay-and-sum beamformer to create a robust regularized superdirective beamformer. We derive analytic closed-form expressions of the beamformer gain responses, and extend them to derive a beamformer with full control of the desired white noise gain or the directivity factor. The proposed approach offers a simple and robust broadband beamformer with controllable characteristics, shown here through persuasive simulation results.
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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 | 0.001 |
| 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 it