Beamforming based on null-steering with small spacing linear microphone arrays
Bibliographic record
Abstract
This paper develops an approach to beamforming with small spacing uniform linear microphone arrays based on the null-steering (NS) principle. It first formulates the beamforming problem from the conventional mean-squared error (MSE) criterion and its normalized version. Several NS algorithms are then derived for beamforming with the constraint of placing nulls to either a single direction or multiple angles. The difference and relationships between different algorithms are discussed and their performances are evaluated. These algorithms can be used to design either fixed or adaptive beamformers. In the former case, the resulting beamformers work as differential microphone arrays (DMAs) since they exhibit frequency-invariant beampatterns and their directivity factors are high with a given number of sensors. In the latter case, the resulting beamformers can be viewed as a combination of DMAs and single-channel noise reduction since they do not only exhibit frequency-invariant beampatterns but also can achieve noise reduction based on the noise statistics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".