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VM-ARRAYDPS introduces virtual microphones with higher signal‑to‑noise ratios to augment a limited physical microphone array, providing additional multi‑channel consistency constraints for blind speech separation. By leveraging a pretrained speech diffusion model and posterior sampling, the method improves upon the existing ArrayDPS approach. Experiments on 2‑speaker and 3‑speaker datasets show significant performance gains, and ablation studies reveal the impact of virtual microphone count and consistency weighting.
arXiv:2602. 01394v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of audio-visual single-microphone speech separation and enhancement in the presence of real-world environmental noise.
arXiv:2609.07226v1 Announce Type: cross Abstract: This paper proposes a general framework for stable and effective iterative audio separation with mixture consistency by extending source separation m...
The paper introduces CF-AVSS, a two‑stage audio‑visual speech separation framework that first generates coarse estimates using a synchronized visual‑stream SPAV‑TFGridNet, then refines these estimates with a TFGridNet‑based target‑speaker refinement network. The final output is a fusion of coarse and refined estimates calibrated on a development set. In the ISCSLP 2026 Real‑World AVSE Challenge, the system tied for first place on Track 1 and ranked second on Track 2, achieving the lowest character error rates of 12.31 % and 14.79 % respectively.
arXiv:2609.30631v1 Announce Type: cross Abstract: Online audio-visual target-speaker extraction aims to remove competing voices while preserving speech quality and bounding lookahead. Existing extrac...
arXiv:2606. 23712v1 Announce Type: cross Abstract: Audio-visual speech enhancement (AVSE) exploits visual cues such as lip movements to recover speech in noisy environments.