Study of the maximum signal-to-noise-ratio filters for single- and multi-channel noise reduction
Bibliographic record
Abstract
Noise reduction is a problem of recovering a speech signal of interest from its noisy observations. Since the objective of the problem is to reduce noise, thereby improving the signal-to-noise ratio (SNR), it is natural to consider the use of maximum SNR filters. However, the maximum SNR filters, if not designed properly, may introduce significant speech distortion, leading to speech quality degradation instead of improvement. This paper investigates the design of maximum SNR filters for noise reduction with minimum speech distortion. It covers the following design cases: (1) single-channel noise reduction in the time domain; (2) multichannel noise reduction in the time domain; (3) single-channel noise reduction in the short-time-Fourier-transform (STFT) domain with or without interframe information; (4) multichannel noise reduction in the STFT domain with or without interframe information. A large number of experiments are performed to illustrate the properties of these maximum SNR filters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".