MPPCAD: Minimum Power Pattern Constrained Adaptive Differential Beamforming
Bibliographic record
Abstract
This paper investigates the design of adaptive differential beamforming using small-spacing linear microphone arrays. We express the differential beamformer as a linear function of the target beampattern coefficients through orthogonal polynomial expansions. Consequently, the design of the beamformer reduces to optimizing these coefficients. To ensure that the maximum array response consistently aligns with the look direction, we derive constraints on the target beampattern coefficients, resulting in two convex sets for the first two orders of beampatterns. This approach uncovers numerous effective beampatterns beyond traditional options such as dipole, cardioid, supercardioid, and hypercardioid. By minimizing the power of the array output while adhering to the beampattern constraints, we develop the MPPCAD beamformer. Simulation results demonstrate that the proposed beamformer significantly enhances speech quality compared to classical differential 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".