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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".