Post-Filtering for Residual ECHO Suppression in Under-Modeled Acoustic ECHO Cancellers
Bibliographic record
Abstract
This paper investigates the use of post-filtering algorithms for suppressing residual echo in adaptive acoustic echo cancellers, which is an important component of hands-free telephone systems. The goal of post-filtering is to enhance near-end speech corrupted by residual echo due to echo canceller under-modeling. In this paper the use of signal subspace enhancement is investigated as a post-filter structure by employing a subspace decomposition of the echo canceller error signal using the Karhunen-Loeve transform (KLT). In particular, the spectral-domain constrained (SDC) linear estimator is employed to enhance the near-end speech subspace while suppressing the residual echo subspace. Practical and low-complexity implementation considerations are discussed. Simulation results are presented comparing the approach to frequency-domain post-filtering in terms of objective near-end speech distortion and quality measures
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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 | 0.000 | 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".