A multistage minimum variance distortionless response beamformer for noise reduction
Bibliographic record
Abstract
This paper develops a multistage approach to the implementation of the minimum variance distortionless response (MVDR) beamformer. It first divides the microphone array of M sensors into M/2 subarrays with each subarray having only two microphones, and a two-channel MVDR beamformer is performed with each subarray. The M/2 subarrays' outputs are then treated as the inputs of M/4 subarrays of two channels in the next stage. Similarly, a two-channel MVDR beamformer is performed with each subarray in the second stage. This process is repeated till the last stage that has only a single output. This multistage MVDR beamformer has the following properties: (1) Its performance is identical to that of the conventional MVDR beamformer in spatially uncorrelated noise; (2) it is much more robust than the conventional MVDR beamformer in diffuse noise, i.e., it has a significantly higher white noise gain as compared to the traditional MVDR beamformer; and (3) its complexity is an order of magnitude smaller than that of the traditional MVDR beamformer. This basic principle can also be easily generalized to the case where every subarray has more than two microphones.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".