An Approach to Microphone Array Geometry Transform
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Bibliographic record
Abstract
Microphone array signal processing is significantly influenced by array parameters such as the number of sensors, array aperture, and array topology. These factors affect both the development of algorithms and their performance limitations. Consequently, applying algorithms designed for one array geometry to signals captured by different geometries can be challenging. To address this issue, we introduce a novel method referred to as the microphone array geometry transform, or simply the microphone array transform. This approach involves two key steps: 1) encoding the array observations in the frequency and angle domains, and 2) generating observations for the target array using array manifold vectors and the frequency-angle domain source signals. We formulate this problem as one of convex optimization and solve it using the well-established alternating direction method of multipliers (ADMM). Simulation results justify the effectiveness of the proposed method.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it