Low-Complexity Adaptive Beamformer for Joint Reverberation and Noise Suppression
Bibliographic record
Abstract
Reverberation and background noise can severely affect the quality and intelligibility of recorded speech, potentially impairing speech communication and human-machine interaction systems. The minimum variance distortionless response (MVDR) beamformer is commonly used to jointly suppress reverberation and noise, but it is computationally intensive due to the need for matrix inversion at each time frame and subband. In this paper, we introduce a beamforming method that combines an MVDR beamformer optimized for noise reduction with a fixed maximum directivity-factor beamformer for reverberation suppression. This hybrid beamformer offers a more efficient implementation than the traditional MVDR beamformer and adapts to varying levels of reverberation and noise by adjusting a single weighting factor. Simulation results demonstrate that the proposed beamformer achieves significantly faster processing speeds compared to the optimal MVDR beamformer, with only minor performance degradation in noise reduction and reverberation suppression.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".