A flexible high directivity beamformer with spherical microphone arrays
Bibliographic record
Abstract
The maximum directivity (MD) beamformer with spherical microphone arrays has many salient features in processing broadband acoustic and speech signals while suppressing noise and reverberation; but it is sensitive to sensors' self-noise and mismatch among these sensors. One effective way to deal with this sensitivity is by increasing the number of microphones, thereby improving the so-called white noise gain (WNG), but this increase may lead to many other design issues in terms of cost, array aperture, and possibly other performance degradation. This paper is tackling this sensitivity problem and presents a flexible high directivity (HD) beamforming algorithm. By approximating the ideal directivity pattern and the beamformer's beampattern with spherical harmonic series, the relationship between the two is obtained. This relationship is subsequently used to deduce a flexible HD beamformer, which can improve the WNG while achieving a directivity factor (DF) between the DF of an Nth-order MD beamformer and that of an (N-1)th-order MD one. Also derived is the analytical link between the DF and a tuning parameter and the link between the WNG and this parameter. Based on these links, one can easily determine the optimal value of the tuning parameter once the value of the DF or the WNG is specified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".