Performance analysis of a dilated attention fast GAN for speech enhancement
Bibliographic record
Abstract
Recent advancements in speech enhancement have witnessed the emergence of generator-based methodologies. However, several of these approaches exhibit complexity in handling input variations, either excelling at low signal-to-noise ratios (SNRs) by utilizing intricate representations of noisy and clean speech or demonstrating superior performance only at higher SNRs. In this work, we investigated speech enhancement using a Dilated Attention Fast Generative Adversarial Network (DAF-GAN). The proposed DAF-GAN framework achieves stability in performance across different SNR conditions by efficiently processing large-scale signal lengths. The DFS-GAN features a dilated discriminator model operating via patches. The generator architecture incorporates multi-decoding and attention gates facilitated through skip-connections, strategically integrated within the Fast-U-Net model to optimize processing speed. An ideal ratio mask was used in the test phase to further refine the enhanced signal by emphasizing target speech while suppressing residual noise or artifacts. The DAF-GAN performance was assessed using objective metrics such as PESQ on a number of noisy speech databases. Results revealed that the DAF-GAN performed modestly in comparison with the state-of-the-art models. For example, analyses of the VoiceBank-DEMAND dataset yielded a PESQ score of 2.50 for the DAF-GAN.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".