Blind deconvolution using Bayesian methods with application to the dereverberation of speech
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Bibliographic record
Abstract
A blind deconvolution algorithm is presented to address the problem of the dereverberation of speech. A Bayesian algorithm is developed for estimating the source, and the problem of ill-conditioning due to long tails of an acoustic impulse response (AIR) is avoided by marginalizing out the unknown channel parameters. The initial samples of the MAP estimate are determined using a stochastic MCMC (Markov chain Monte Carlo) technique, and these estimates are then used in a sequential procedure for estimating the remaining signal. A filterbank implementation is used to reduce the large deconvolution problem into several smaller independent problems. Simulation results are presented to demonstrate the performance of the algorithm applied to the dereverberation of speech.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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