Differential beamforming with circular microphone arrays
Bibliographic record
Abstract
Differential beamforming cannot only achieve high directional gain but also can form frequency-invariant beampatterns. Therefore, it has the great potential to be widely used in voice communications to process broadband speech signals. The performance of a differential beamformer in terms of directivity factor (DF), white noise gain (WNG), and frequency invariance of the beampattern depends on many factors including the array geometry, the number of sensors, the inter-sensor spacing, etc. In the literature, most studies on differential beamforming have been focused on linear microphone arrays and not much efforts have been devoted to other array geometries. This paper is devoted to the study of circular microphone arrays. In comparison with linear arrays whose spatial response is a function of the steering angle, circular microphone arrays can have the same spatial response along many different directions. The focal point of this paper is on differential beamforming with uniform circular microphone arrays. The main topics addressed include: (1) major properties of circular microphone arrays, (2) how to design differential beamformers with different beampatterns, (3) how to design differential beamformers with different orders, and (4) how to achieve a good control of the white noise amplification problem, high directional gains, and frequency-independent responses.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".