Beamforming for directional sources: Additional estimator and evaluation of performance under different acoustic scenarios
Bibliographic record
Abstract
Beamforming is done with an array of sensors to achieve a directional or spatially-specific response by using a model of the arriving wavefront. Conventionally, a plane wave or point source model is used and this can cause decreased array gain or even total breakdown of beamforming when the source is directional. To avoid this, the authors proposed in recent work an alternative beamforming method which defines a set of "sub-beamformers," each designed to respond to a different spatial mode of the source. The outputs of the individual sub-beamformers are combined in a weighted sum to give an overall output of better quality than that of a monopole beamformer. This paper extends the previous work by introducing an additional estimator for the weighted sum and by presenting simulation results to demonstrate the relative performance of the proposed method and the different estimators for a directional source in the presence of diffuse noise, reverberation, and an interfering source. Gain optimization subject to a constraint on the white-noise gain with the proposed beamforming method is also introduced. Generally, when beamforming on directional sources, the proposed method outperforms beamforming with a point source model when the input signal-to-noise ratio (SNR) is 0 dB or higher.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".