Nonlinear echo cancellation using a partial adaptive time delay neural network
Why this work is in the frame
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Bibliographic record
Abstract
System identification of a nonlinear loudspeaker/microphone acoustic system is necessary to achieve high acoustic echo cancellation in the handsfree telephony environments where the loudspeaker often operates at high volumes. In this paper, a partial adaptive process consisting of a small order tapped delay line neural network (TDNN) followed by a delayed normalized least mean squares (NLMS) adaptive filter is used to model a loudspeaker/microphone acoustic system. The TDNN models the first part of the acoustic impulse response (AIR) where most of the energy is contained and the delayed NLMS filter models the remaining echo. Experimental measurements confirm that a short length TDNN is capable of improved identification in an undermodelled system and that by extending this to the partial adaptive TDNN structure, the ERLE performance improves by 5.5 dB at high loudspeaker volumes when compared to a NLMS structure.
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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