Microphone array beamforming based on maximization of the front-to-back ratio
Bibliographic record
Abstract
Microphone arrays are typically used in room acoustic environments to acquire high fidelity audio and speech signals while suppressing noise, interference, and reverberation. In many application scenarios, interference and reverberation may mainly come from a certain region, and it is therefore necessary to develop beamformers that can preserve signals of interest while minimizing the power of signals coming from the region where interference and reverberation dominate. For this purpose, this paper first reexamines the so-called front-to-back ratio and the classical supercardioid beamformer. To deal with the white noise amplification problem and the limited directivity factor associated with the supercardioid beamformer, a set of reduced-rank beamformers are deduced by using the well-known joint diagonalization technique, which can make compromises between the front-to-back ratio and the amount of white noise amplification or the directivity factor. Then, the definition of the front-to-back ratio is extended to a generalized version, from which another set of reduced-rank beamformers and their regularized versions are developed. Simulations are conducted to illustrate the properties and advantages of the proposed beamformers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".