An efficient, low-complexity, normalized LMS algorithm for echo cancellation
Why this work is in the frame
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Bibliographic record
Abstract
Modem teleconferencing systems contain multiple audio channels. Algorithms for acoustic echo cancellation form an integral part of these systems. They need to be computationally efficient and rapidly converging. Normalized least mean square (NLMS) algorithms, because of their simple architecture and robust performance, form the backbone of the echo cancellers used in the industry. They become computationally expensive if used for echo cancellation in multi-channel systems. The algorithm proposed in this paper addresses this problem by incorporating the principle of partial updating of the filter coefficients in the NLMS algorithm. The performance of the proposed algorithm is compared with other adaptive algorithms for acoustic echo cancellation. It is shown that the proposed algorithm has a reduced complexity, while providing a good overall performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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