aiqbench analysis
MeCo introduces a one-step generative corrector that maps discriminative speech separation estimates directly onto the clean speech manifold, addressing the common gap between high reference-based metrics and actual human listening quality. By integrating Data-Space Optimization with specific loss functions targeting displacement intervals and terminal fidelity, the method achieves state-of-the-art performance with minimal computational overhead. This approach offers a practical pathway for improving audio processing pipelines where perceptual quality is prioritized alongside signal accuracy.
MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation
arXiv:2606.09677v2 Announce Type: replace-cross Abstract: 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 terminal signal fidelity. Experiments demonstrate that MeCo achieves state-of-the-art (SOTA) performance with minimal computational overhead, simultaneously achieving superior signal fidelity and human listening quality in both in-domain and out-of-domain scenarios.
Abstract: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 terminal signal fidelity. Experiments demonstrate that MeCo achieves state-of-the-art (SOTA) performance with minimal computational overhead, simultaneously achieving superior signal fidelity and human listening quality in both in-domain and out-of-domain scenarios.
Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
arXiv:2606.09677 [eess.AS]
(or arXiv:2606.09677v2 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2606.09677 Focus to learn more arXiv-issued DOI via DataCite
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