A Data-Reuse Semi-Blind Source Separation Approach for Nonlinear Acoustic Echo Cancellation
Why this work is in the frame
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Bibliographic record
Abstract
Nonlinear acoustic echo cancellation (NAEC) is of significant importance in acoustic telecommunication. To improve NAEC performance in the double-talk case, semi-blind source separation-based NAEC (SBSS-NAEC) algorithms have been proposed. However, to deal with reverberation and loudspeaker nonlinearities, convolutive transfer function (CTF) models and power series expansions are employed, which significantly increase the number of free parameters and consequently lead to slow convergence speed and, hence, limited performance. In this paper, we introduce the data-reuse strategy, well-known in the adaptive filter literature, into an SBSS-NAEC framework and propose two algorithms: data-reuse iteration projection (DR-IP) and data-reuse element-wise iterative source steering (DR-EISS). Several simulations demonstrate the superiority of the proposed methods, especially the tracking capability when the impulse response changes.
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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 | 0.000 | 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