Design of robust concentric circular differential microphone arrays
Bibliographic record
Abstract
Circular differential microphone arrays (CDMAs) have been extensively studied in speech and audio applications for their steering flexibility, potential to achieve frequency-invariant directivity patterns, and high directivity factors (DFs). However, CDMAs suffer from both white noise amplification and deep nulls in the DF and in the white noise gain (WNG) due to spatial aliasing, which considerably restricts their use in practical systems. The minimum-norm filter can improve the WNG by using more microphones than required for a given differential array order; but this filter increases the array aperture (radius), which exacerbates the spatial aliasing problem and worsens the nulls problem in the DF. Through theoretical analysis, this research finds that the nulls of the CDMAs are caused by the zeros in the denominators of the filters' coefficients, i.e., the zeros of the Bessel function. To deal with both the white noise amplification and deep nulls problems, this paper develops an approach that combines different rings of microphones together with appropriate radii. The resulting robust concentric circular differential microphone arrays (CCDMAs) can mitigate both problems. Simulation results justify the superiority of the robust CCDMA approach over the traditional CDMAs and robust CDMAs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".