Compressive sensing‐based speech enhancement in non‐sparse noisy environments
Bibliographic record
Abstract
In the authors previous work, a compressive sensing (CS)‐based method has been proposed to address speech enhancement (SE) in adverse environments (CS‐SPEN) based on an assumption of sparse noise. However, this assumption may not be satisfied in practical noisy environments. In this study, the authors study this issue by relaxing this assumption to consider a general non‐sparse noise case, such that the proposed method naturally extends the previous one. In particular, they solve the theoretic difficulty of CS‐SPEN on the treatment of non‐sparse noise by using a relaxed upper bound for the constraint governing data consistency and a relaxed estimation error bound. Their main result is mathematically proved. In addition, the effectiveness of the proposed method is demonstrated by computational simulations, showing certain improvements to the previous method for both stationary and non‐stationary white Gaussian noises across various segmental signal‐noise‐ratios (SNRs). In these cases, the proposed method is shown to have comparable results to the state‐of‐the‐art SE alogrithms and some advantages over them at low SNRs. CS‐SPEN without the sparse noise assumption works evenly with CS‐SPEN with the sparse noise assumption for car internal and F16 cockpit noises.
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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 | 0.000 |
| 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) | 0.001 | 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".