单步生成语音增强，速度快且质量高。
MeanFlowSE: one-step generative speech enhancement via conditional mean flow
- 学习轨迹上有限区间平均速度，替代传统迭代求解。
- 单步推理下在VoiceBank-DEMAND上表现优异，计算成本更低。
- 无需知识蒸馏或外部教师，适合实时语音增强场景。
多步推理是生成式语音增强在实时应用中的瓶颈，因为基于流和扩散的系统学习瞬时速度场，依赖迭代常微分方程求解器。本文提出MeanFlowSE，一种条件生成模型，学习轨迹上有限时间区间的平均速度。通过雅可比向量积（JVP）实现均值流恒等式，推导出直接监督有限区间位移的局部训练目标，同时保持对角线上的瞬时场约束一致性。推理时，MeanFlowSE通过逆时位移实现单步生成，无需多步求解器；可选的少数步变体提供额外优化。在VoiceBank-DEMAND数据集上，单步模型在语音可懂度、保真度和感知质量方面表现良好，计算开销显著低于多步基线方法。该方法无需知识蒸馏或外部教师，提供一种高效、高保真的实时生成式语音增强框架。代码已开源：https://github.com/liduojia1/MeanFlowSE。
原文摘要 · Abstract (English)
Multistep inference is a bottleneck for real-time generative speech enhancement because flow- and diffusion-based systems learn an instantaneous velocity field and therefore rely on iterative ordinary differential equation (ODE) solvers. We introduce MeanFlowSE, a conditional generative model that learns the average velocity over finite intervals along a trajectory. Using a Jacobian-vector product (JVP) to instantiate the MeanFlow identity, we derive a local training objective that directly supervises finite-interval displacement while remaining consistent with the instantaneous-field constraint on the diagonal. At inference, MeanFlowSE performs single-step generation via a backward-in-time displacement, removing the need for multistep solvers; an optional few-step variant offers additional refinement. On VoiceBank-DEMAND, the single-step model achieves strong intelligibility, fidelity, and perceptual quality with substantially lower computational cost than multistep baselines. The method requires no knowledge distillation or external teachers, providing an efficient, high-fidelity framework for real-time generative speech enhancement. The proposed method is open-sourced at https://github.com/liduojia1/MeanFlowSE.
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