SEformer: Dual-Path Conformer Neural Network is a Good Speech Denoiser
Bibliographic record
Abstract
In this paper, we propose the SEformer, an efficient dual-path conformer neural network for speech enhancement. The proposed SEformer is comprised of an encoder, a decoder and dual-path conformer blocks in between. First, the encoder receives the noisy speech waveform to generate the speech features. Then, each proposed dual-path conformer block employs a temporal and frequency conformer in parallel to simultaneously extract temporal and frequency features of speech sequences, which are then fused by a transformer block for contextual representation. Finally, the decoder transforms the contextual speech features into a mask to filter out noise-related information of noisy speech, generating the estimated clean speech. Experimental results on the benchmark dataset exhibit that our SEformer yields a competitive performance to existing state-of-the-art methods while containing the fewest model parameters (about 590k).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".