DBAUNet: Dual-branch attention U-Net for time-domain speech enhancement
Bibliographic record
Abstract
Recently, many speech enhancement methods in-volve attention mechanism to learn long-term dependencies of speech signals. And the U-Net structure is widely used for extracting hierarchical features. In this paper, we propose a dual-branch attention U-Net for speech enhancement in the time domain, named as DBAUNet, which consists of a convolutional en-coder, a dual-branch attention block and a convolutional de-coder. The encoder is used to extract the compressed features from input noisy speech. Then, the dual-branch attention block employs spatial-wise and channel-wise attention to extract the spatial and channel information of speech sequences in parallel, which are fused to learn the contextual information. Then a de-coder which has a symmetric structure with the encoder, is adopted to reconstruct the enhanced speech waveform. Experimental results on the benchmark dataset demonstrate that our proposed DBAUNet achieves a comparable performance to existing models while involving the fewest 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".