A family of Bayesian STSA estimators for the enhancement of speech with correlated frequency components
Bibliographic record
Abstract
In Bayesian short-time spectral amplitude (STSA) estimation for speech enhancement, the spectral components are traditionally assumed uncorrelated. However, this assumption is inexact since some correlation is present in practice. We thus investigate a multi-dimensional STSA estimator that assumes correlated frequency components. Since the closed-form solution of this optimum estimator is not readily available, we previously derived closed-form expressions for an upper and a lower bound on the desired estimator. In this paper, we study the proximity between the upper and the lower bounds and propose a new family of estimators that are derived from these bounds and characterized by a scalar parameter 0 ≤ γ ≤ 1, with γ = 0 corresponding to the lower bound and γ = 1 to the upper bound. Experimental results show that the proposed estimators achieve a better performance than existing estimators, especially at high SNR.
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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) | 0.000 | 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".