Design and Optimization of Superdirective Beamforming and Post-Filtering for Speech Enhancement
Bibliographic record
Abstract
Superdirective beamformers, used with small microphone arrays, are highly attractive due to their high directivity and frequency-invariant beampatterns, making them well-suited for processing broadband acoustic and speech signals. However, these beamformers are very sensitive to array imperfections such as sensor mismatches and self-noise. To improve robustness, robust superdirective (RSD) beamformers have been developed, employing techniques such as diagonal loading or white-noise-gain constraints during their derivation. Although RSD beamformers offer enhanced robustness compared to classical superdirective beamformers, they cannot achieve the maximum directivity factor and lose some frequency-invariant properties, resulting in a beamwidth that is wider at low frequencies and narrower at high frequencies. As a result, RSD beamformers do not fully meet the criteria of true superdirective beamformers, providing less effective noise reduction and introducing some speech distortion. Post-filtering methods have been developed to improve noise reduction after RSD beamforming, but they often fail to address the distortion issues, especially when the speech source deviates from the array’s look direction. To overcome this limitation, this paper proposes a joint optimization approach that combines post-filtering with RSD beamformers. By using the output of RSD beamformers as input data and considering various deviations in look directions and array mismatches, we train a post-filtering network to further enhance the beamformer’s output. Experimental results on speech enhancement demonstrate the effectiveness and robustness of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".