Drift-Compensated Adaptive Filtering for Improving Speech Intelligibility in Cases with Asynchronous Inputs
Why this work is in the frame
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Bibliographic record
Abstract
In general, it is difficult for conventional adaptive interference cancellation schemes to improve speech intelligibility in the presence of interference whose source is obtained asynchronously with the corrupted target speech. This is because there are inevitable timing drifts between the two inputs to the system. To address this problem, a drift-compensated adaptive filtering (DCAF) scheme is proposed in this paper. It extends the conventional schemes by adopting a timing drift identification and compensation algorithm which, together with an advanced adaptive filtering algorithm, makes it possible to reduce the interference even if the magnitude of the timing drift rate is as big as one or two percent. This range is large enough to cover timing accuracy variations of most audio recording and playing devices nowadays.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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