A general approach to the design and implementation of linear differential microphone arrays
Bibliographic record
Abstract
The design of differential microphone arrays (DMAs) and the associated beamforming algorithms have become very important problems. Traditionally, an Nth order DMA is formed by subtractively combining the outputs of two DMAs of order N-1. This method, though simple and easy to implement, suffers from a number of limitations. For example, it is difficult to design the equalization filter that is needed for compensating the array's non-uniform frequency response, particularly for high-order DMAs. In this paper, we propose a new approach to the design and implementation of linear DMAs for speech enhancement. Unlike the traditional method that works in the time domain, this proposed approach works in the short-time Fourier transform (STFT) domain. The core issue with this framework is how to design the desired differential beamformer in each subband, which is accomplished by solving a linear system consisting of N +1 fundamental constraints for an Nth-order DMA.
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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".