D2L2-Dense LSTM Deep Learning Based Nonlinear Acoustic Echo Cancellation
Bibliographic record
Abstract
Speech quality is a crucial concern, as voice communication is a more noteworthy and ubiquitous aspect of everyday life.The emergence of audible echoes is one of the factors contributing to uncomplimentary quality deterioration.Network hardware and end-user devices are intrinsically prone to this sort of quality deterioration.Designing efficient acoustic echo cancellation (AEC) devices is vital for improving listening comfort and voice quality.When we utilize inexpensive and small analog components, an echo canceller operates poorly or not at all in the system if the net nonlinear distortion is greater than a certain value.Many adaptive filters are used to remove the echo from the microphone signal to solve this problem.Nonetheless, it is difficult to accomplish the preeminent performance of the AEC in real-time circumstances.In this work, we propose nonlinear acoustic echo cancellation (NAEC) using dense long short-term memory (LSTM)-based deep learning (D 2 L 2 ).Deep learning has been applied to the concept of speech source separation (SSS).In our deep learning based NAEC, the near-end signal is separated from the microphone using LSTM layer training.Before learning commences, the Short-Time Fourier Transform (STFT) is used to extract frequency-time domain features from the acoustic signal.In the learning part of D 2 L 2 , two targets are assigned.The spectral Magnitude Mask (MM) is the primary, and the Near-end Signal Mask (NSM) is the secondary mask.The simulation shows that our D 2 L 2 achieves a higher Echo Return Loss Enhancement (ERLE) than other works.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".