Quality-delay-and-computation trade-off analysis of acoustic echo cancellation on general-purpose CPU
Bibliographic record
Abstract
While many previous studies have examined acoustic echo cancellation (AEC) in terms of quality, computation complexity, and implementation issues on DSP processors, this work evaluates quality-delay-computation trade-off of unconstrained frequency-domain recursive-least-square AEC algorithm on general purpose microprocessors. Specially, trade-off among echo cancellation quality, sampling delay, and computation time on Intel Pentium 4 systems is analyzed. Our quantitative analysis shows that the effectiveness of echo cancellation does not depend on availability of CPU as long as CPU can provide sufficient computational power for online real-time processing. Today's general-purpose microprocessor-based AEC can deliver satisfactory echo cancellation quality at a computationally acceptable price (less than 5% CPU usage). On other hand, the effectiveness depends on sampling delay. And no matter how fast a microprocessor would be, it is unlikely to guarantee both smaller sampling delay and larger echo-return-loss-enhancement (ERLE) at the same time. Finally, considering possible application of general-purpose processor-based AEC in laptop, office and meeting room environments, we analyzed acoustic channel delay's influence on both ERLE and CPU computation, showing that general- purpose microprocessor AEC's outstanding ability in tolerating various computing environments. Our experimental results can be used to design good configuration to meet specific quality requirements in terms of quality and sampling delay.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".