MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation
While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening quality. To address this, we propose a novel MeanFlow-based one-step generative corrector (MeCo). MeCo learns a conditional average velocity field to map discriminative estimates directly onto the clean speech manifold in a single step. To maximize one-step generation performance, we introduce Data-Space Optimization (DSO). DSO integrates an $\mathbf{x}_r$-loss, which penalizes prediction errors on longer displacement intervals to serve as a generative objective for human listening quality, with an Endpoint SI-SDR loss that directly optimizes term...
| Authors | Dohwan Kim, Jung-Woo Choi |
|---|---|
| Primary category | eess.AS |
| Published | 8 June 2026 |
| Last updated at source | 9 July 2026 |
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