On Limitations and Improvement of Differential Beam Forming Via Quadratic Eigenvalue Optimization
Bibliographic record
Abstract
Differential microphone arrays (DMAs) have attracted considerable attention for their high spatial gains and frequency-invariant spatial responses. However, they often face significant white noise amplification at low frequencies. One approach to mitigating this challenge is by increasing the number of microphones while fixing the order of the DMA, leveraging additional degrees of freedom to optimize the white noise gain (WNG). But this compensation of WNG can lead to beampattern distortion at mid and high frequencies. To address this issue, we recently explored an approach to designing differential beamformers with predefined WNG levels. This involves formulating beamforming as a quadratic eigenvalue problem (QEP) to efficiently derive optimal solutions without iterative processes, leading to the QEP-based differential beamformers. While it is successful in controlling WNG, this method is found to exhibit great and atypical performance degradation at lower frequencies in certain scenarios. In this paper, we illustrate this phenomenon using a two-stage structured beamformer as a case study and offer insights into why this occurs, along with proposing a solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".