Modulation spectrum based beamforming for speech enhancement
Bibliographic record
Abstract
In array signal processing, beamforming is a common technique to align time differences between multi-microphone signals. Beamformers, however, have limits to reduce noise specially in the presence of reverberation. In this paper, we incorporate modulation properties of speech into a pre-processing algorithm to improve beamformer performance under combined noise-plus-reverberation conditions. In the modulation domain, signals are decomposed into modulators and carriers. Here, we propose to filter and perform short-time spectral subtraction of the modulator as a pre-processing step prior to beamforming, which in turn, is designed to align time differences between carriers of the array signal and to minimize the residual noise of the pre-processed signals. Simulation results with several noise-only and noise-plus-reverberation conditions show that the modulation pre-processing has improved the minimum power distortionless response beamformer by up to 7.4dB in the signal-to-noise ratio and 0.6 points in perceptual evaluation of speech quality (PESQ) score.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".