A new adaptive beamformer for optimal acoustic echo and noise cancellation with less computational load
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".