On the Design of Robust Differential Beamformers From the Beampattern Error Perspective
Bibliographic record
Abstract
Differential microphone arrays (DMAs), which enhance acoustic signals of interest by measuring both the acoustic pressure field and its spatial derivatives, find extensive use in various practical systems and acoustic products. A critical element of DMAs is the differential beamformer, traditionally designed to ensure that the designed beampattern closely matches the desired target directivity pattern. However, such beamformers may lack sufficient robustness in practice. To address the balance between robustness and beampattern accuracy, this letter proposes two types of beamformers: one prioritizes maximizing the white noise gain (WNG) while maintaining a specified mean-squared beampattern error (MSBE), and the other aims to minimize MSBE while adhering to a specified level of WNG. By transforming these design challenges into quadratic eigenvalue problems (QEPs), we derive explicit solutions for the proposed beamformers. Simulations are conducted to illustrate the performance characteristics of these beamformers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".