Combined Differential Beamforming With Uniform Linear Microphone Arrays
Bibliographic record
Abstract
While differential beamformers have been widely used in voice communication and human-machine speech interface systems to enhance speech signals of interest, how to design such beamformers that on the one hand can achieve the highest possible directivity factor (DF) and on the other hand are able to obtain a certain level of white noise gain (WNG), so that they are robust enough to sensors’ self noise and array imperfections is still a challenging issue. This paper studies the problem of robust differential beamforming with small-size arrays to achieve a high DF. It presents a method for the design of differential beamformers with uniform linear arrays. We first generate differential pressure signals by applying the recently developed forward spatial difference operator to the outputs of the array with pressure sensors. The pressure microphone observation signals and the differential pressure signals are then put together, and a combined beamformer is subsequently designed, which consists of two subbeamformers, one operates on the pressure microphone observations and the other on the differential pressure signals. A new class of combined differential beamformers are introduced, which can achieve different levels of compromises between DF and WNG using an adjustable parameter.
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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".