Bayesian short-time spectral amplitude estimators for single-channel speech enhancement
Bibliographic record
Abstract
Single-channel speech enhancement algorithms are used to remove background noise in speech. They are present in many common devices such as cell phones and hearing aids. In the Bayesian short-time spectral amplitude (STSA) approach for speech enhancement, an estimate of the clean speech STSA is derived by minimizing the statistical expectation of a chosen cost function. Examples of such estimators are the minimum mean square error (MMSE) STSA, the β-order MMSE STSA (β-SA), which includes a power law parameter, and the weighted Euclidian (WE), which includes a weighting parameter. This thesis analyzes single-channel Bayesian STSA estimators for speech enhancement with the aim of, firstly, gaining a better understanding of their properties and, secondly, proposing new cost functions and statistical models to improve their performance. In addition to a novel analysis of the β-SA estimator for parameter β ≤ 0, three new families of estimators are developed in this thesis: the Weighted β-SA (Wβ-SA), the Generalized Weighted family of STSA estimators (GWSA) and a family of multi-dimensional Bayesian STSA estimators. The Wβ-SA combines the power law of the β-SA and the weighting factor of the WE. Its parameters are chosen based on the characteristics of the human auditory system which is found to have the advantage of improving the noise reduction at high frequencies while limiting the speech distortions at low frequencies. An analytical generalization of a cost function structure found in many existing Bayesian STSA estimators is proposed through the GWSA family of estimators. This allows a unification of Bayesian STSA estimators and, moreover, provides a better understanding of this general class of estimators. Finally, we propose a multi-dimensional family of estimators that accounts for the correlated frequency components in a digitized speech signal. In fact, the spectral components of the clean
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".