New Perspectives and Insights on Distortionless Microphone Array Beamforming
Bibliographic record
Abstract
This paper studies distortionless beamforming within a general signal model, where a target source is accompanied by multiple interference sources and ambient noise. In this context, the minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) beamformers are commonly used techniques. However, the MVDR beamformer lacks control over residual interference sources, while the LCMV beamformer may result in insufficient noise attenuation and, in some cases, even amplify ambient noise at its output. To provide a flexible mechanism for balancing ambient noise reduction and interference suppression, we introduce a novel optimization criterion, which leads to the development of a general distortionless beamformer that includes both the MVDR and LCMV methods as special cases. The effectiveness of our proposed approach is demonstrated through various performance metrics and beampatterns.
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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".