An Integrated CNN-GRU Framework for Complex Ratio Mask Estimation in Speech Enhancement
Bibliographic record
Abstract
In this paper, we propose a novel neural network-based speech enhancement approach, where a convolutional neural network (CNN) and a gated recurrent unit (GRU) are integrated to estimate a modified complex ratio mask (MCRM.) The new CNN structure comprised of frequency dilated convolution layers is employed to extract speech features while benefiting from the global contextual information of input speech. The CNN incorporates the skip connection and residual learning techniques to facilitate the training and accelerate the convergence. The GRU network is exploited to map the CNN-extracted features to the MCRM, which is used to enhance both magnitude and phase of the input speech. We compare the enhancement performance of the proposed method using features extracted by CNN with that of the GRU network using some conventional acoustic features, showing the advantage of the proposed CNN-GRU model. We also demonstrate that the GRU outperforms other recurrent neural network variations within the proposed model for mask estimation in terms of separated speech quality, memory footprint, and the number of model parameters in the presence of highly non-stationary noises.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".