PhysMVNet: Physics-Informed End-to-End MVDR Beamformer with Residual Spectral Mapping for Multichannel Speech Enhancement
Bibliographic record
Abstract
We propose PhysMVNet, a physics-inspired end-to-end framework for multichannel speech enhancement that integrates a learnable MVDR beamformer, a Helmholtz-inspired STFT-domain regularizer, and a residual spectral mapping module. The beamformer is trained with a reconstruction loss, while the regularizer encourages local smoothness in the STFT spectrogram to improve robustness to noise and array perturbations. To mitigate spectral distortions introduced by beamforming, we incorporate a three-band residual spectral mapping network to restore fine details. Experiments on CHiME-3/4 show that PhysMVNet achieves state-of-the-art perceptual quality and intelligibility while maintaining a lightweight design suitable for realtime application. It also remains stable under extreme low-SNR conditions and array perturbations. Ablation studies confirm the contribution of each component, highlighting the benefits of physics-inspired priors in deep beamforming networks for robust, high-fidelity speech enhancement.
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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 | 0.000 | 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) | 0.005 | 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".