Robust adaptive Kalman filtering-based speech enhancement algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The paper deals with the problem of speech enhancement when only a corrupted speech signal is available for processing. Kalman filtering is known as an effective speech enhancement technique, in which the speech signal is usually modeled as an autoregressive (AR) model and represented in the state-space domain. Various approaches based on the Kalman filter have been presented in the literature. They usually operate in two steps: first, additive noise and driving process statistics and speech model parameters are estimated and second, the speech signal is estimated by using Kalman filtering. In the paper, sequential estimators are used for suboptimal adaptive estimation of the unknown a priori driving process and of additive noise statistics simultaneously with the system state. The estimation of time-varying AR signal model is based on a robust recursive least-square algorithm with variable forgetting factor. The proposed algorithm provides improved state estimates at little computational expense.
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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 it