Cptnn: Cross-Parallel Transformer Neural Network For Time-Domain Speech Enhancement
Bibliographic record
Abstract
In this paper, we propose a novel cross-parallel transformer neural network (CPTNN) for end-to-end speech enhancement in the time domain. The new structure is comprised of an encoder, a cross-parallel transformer module (CPTM), a masking module and a decoder. The encoder first maps the input waveform of noisy speech into feature representations. The CPTM consists of four residually connected cross-parallel transformer blocks, each utilizing local and global transformers to simultaneously extract local and global features which are then fused by a cross-attention based transformer to obtain a better contextual feature representation. The masking module generates a mask to multiply with encoder output, producing the masked encoder features which will be finally used for reconstructing the enhanced speech by the decoder. Experiments are undertaken on the benchmark dataset, indicating that our CPTNN achieves a better performance than state-of-the-art methods in terms of most evaluation criteria while maintaining the lowest model parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".