Further Analysis of the β-Order MMSE STSA Estimator for Speech Enhancement
Bibliographic record
Abstract
In Bayesian approaches for speech enhancement, the clean speech is estimated by minimizing the expectation of a desired cost function. In the β-order MMSE STSA (βSA) Bayesian estimator, the cost function is the squared difference between the estimated and actual clean speech short-time spectral amplitude (STSA), both to the power β > 0. In this paper we propose an extension of the analysis of the βSA estimator for values of β < 0. We find that when β < 0, a normalization occurs in the βSA estimator which produces more noise reduction as β is reduced at the expense of additional speech distortion. Furthermore, the βSA estimator with β = -1 slightly outperforms the well known MMSE STSA and MMSE log-STSA (LSA) estimators in terms of the PESQ, for the two noises studied, while the overall MOS appreciation for β = -1 is found to be better than both MMSE STSA and LSA for white noise.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".