A new adaptive beamformer for optimal acoustic echo and noise cancellation with less computational load
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigates positive synergies of the combination of acoustic echo canceller with new adaptive beamformer (NABF) for acoustic echo and background noise cancellation. The NABF uses multichannel linear prediction error filters (LPEFs) in the sidelobe canceling path and adaptive noise estimation filters (ANEFs) in the multi-channel noise canceller. Since the AEC module is located behind the fixed beamformer of the NABF only one AEC module is required and the AEC does not feel any repercussions from the NABF. In order to illustrate the effectiveness of the proposed integrated scheme (AECNABF), it is compared to the multi-channel acoustic echo canceller (AEC-first) which provide good performance but at the expense of very high computational complexity. Simulation results show that the performance of the proposed scheme is comparable to that of AEC-first scheme with very less computational complexity.
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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