A Decomposition-Based RLS Algorithm for Stereophonic Acoustic Echo Cancellation
Bibliographic record
Abstract
In hands-free teleconferencing systems, stereo transmission provides telepresence thanks to our binaural hearing system and gives a realistic presence that actual single-channel systems cannot offer. In this context, stereophonic acoustic echo cancellation (SAEC) is necessary for full-duplex quality communication. Nevertheless, the long length of the acoustic impulse responses represents one of the main challenges in SAEC, which significantly impacts the overall performance of the adaptive filters used to model the acoustic echo paths. In this paper, we propose a decomposition-based approach using the nearest Kronecker product, which results in a combination of shorter filters to solve the original system identification problem. The solution is formulated in terms of the recursive least-squares (RLS) algorithm, which is very appealing for its convergence features. Besides the lower computational complexity, the proposed decomposition-based RLS algorithm achieves a better tracking behavior as compared to its conventional counterpart. Simulation results support these practical features.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".