Design of Optimal Linear Differential Microphone Arrays Based Array Geometry Optimization
Bibliographic record
Abstract
This paper presents a method to design optimal linear differential microphone arrays (DMAs) by optimizing the array geometry. By constraining the DMA beamformer to achieve a given target value of the directivity factor (DF) with a specified target frequency-invariant beampattern while achieving also the highest possible white noise gain (WNG), an optimization algorithm is developed, which consists of the following two steps. 1) The full frequency band of interest is divided into a few subbands. At every subband, the entire linear array is divided into subarrays and the number of subarrays depends on the total number of the sensors and the order of the DMA. A cost function is then defined, which is minimized to determine what subarray produces the optimal performance. 2) The subband optimal subarrays are then combined across the entire frequency band to form a fullband cost function, from which the geometry of the entire array is optimized. These two steps are repeated with the particle swarm optimization (PSO) algorithm until the desired array performance is reached. Simulation results demonstrate that the proposed method can obtain the target DF with a frequency-invariant beampattern over a wide band of frequencies while maintaining a reasonable level of WNG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".