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English(EN) Speech Enhancement Based on Drifting Models

DriftSE框架使用生成模型提供一步式语音增强

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新的生成式语音增强框架DriftSE，该框架通过一步推理绕过了迭代采样。这种新颖的方法将去噪表述为一个平衡问题，使用学习到的“漂移场”将样本直接引导至干净语音分布。在VoiceBank-DEMAND基准上的实验表明，DriftSE在保真度和速度上均优于多步扩散模型，有可能为该领域建立新的范式。 AI

影响 建立了一种新的、更快的语音增强范式，可能改进实时音频处理应用。

排序理由 介绍一种新颖的生成式语音增强框架的学术论文。

在 arXiv cs.AI 阅读 →

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DriftSE框架使用生成模型提供一步式语音增强

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介绍一种新颖的生成式语音增强框架的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Liang Xu, Diego Caviedes-Nozal, Bastiaan Kleijn, Longfei Felix Yan, Rasmus Kongsgaard Olsson ·

    基于漂移模型的语音增强

    arXiv:2604.24199v1 Announce Type: cross Abstract: We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Rather than relying on iterative sampling, DriftSE natively achieves one-step infe…

  2. arXiv cs.AI TIER_1 English(EN) · Rasmus Kongsgaard Olsson ·

    基于漂移模型的语音增强

    We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Rather than relying on iterative sampling, DriftSE natively achieves one-step inference by evolving the pushforward distribution of …

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