On microphone array beamforming and insights into the underlying signal models in the short-time-Fourier-transform domain
Bibliographic record
Abstract
This paper studies signal models for microphone array beamforming in the short-time-Fourier-transform (STFT) domain with long acoustic impulse responses. The major contributions are as follows. First, the signal modeling problem is investigated in the STFT domain and a general decomposition is proposed for the convolved source signal. Second, new insights into the array manifold are presented: the STFT of the windowed acoustic impulse response from the source to the sensors. Third, the structure of the reference signal is analyzed: it can be viewed as the output of a beamformer without considering the noise in the observation signal. Fourth, based on the new perspectives and decomposition, a signal model is derived based on the use of the superdirective beamformer. Finally, three performance measures are defined, based on which three optimal/suboptimal signal models are derived and their performance is assessed under different acoustic environments and analysis window lengths. The performance of the well-known minimum variance distortionless response (MVDR) beamformer is evaluated, which justifies the properties of the developed signal models.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".