Real-world particle filtering-based speech enhancement
Bibliographic record
Abstract
This paper presents a viable particle filtering (PF) solution for single microphone speech enhancement in real-world conditions, i.e., operating at low SNR in nonstationary noise environments, while remaining computationally tractable. The enhancement takes place in the subband domain with elementary PFs in each band. To efficiently handle complex noise situations, the noise spectrum is modelled in each band as a white Gaussian noise sequence with a time-varying gain. Two solutions are proposed to estimate these time-varying average subband noise levels: they are either drawn internally by the PFs, or they are obtained by external dedicated noise power spectral density estimation - both methods are found to yield very close results. Several subband decompositions are tested, and a robust way of incorporating perceptual constraining is introduced. The assembled PF-based architecture is then compared with state-of-the-art enhancement algorithms in various conditions, and is found to outperform them according to seven objective speech quality measures.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".