In this work, we build upon our previous publication and use diffusion-based generative models for speech enhancement. We present a detailed overview of the diffusion process that is based on a stochastic differential equation and delve into an extensive theoretical examination of its implications. Opposed to usual conditional generation tasks, we do not start the reverse process from pure Gaussian noise but from a mixture of noisy speech and Gaussian noise. This matches our forward process which moves from clean speech to noisy speech by including a drift term. We show that this procedure enables using only 30 diffusion steps to generate high-quality clean speech estimates. By adapting the network architecture, we are able to significantly improve the speech enhancement performance, indicating that the network, rather than the formalism, was the main limitation of our original approach. In an extensive cross-dataset evaluation, we show that the improved method can compete with recent discriminative models and achieves better generalization when evaluating on a different corpus than used for training. We complement the results with an instrumental evaluation using real-world noisy recordings and a listening experiment, in which our proposed method is rated best. Examining different sampler configurations for solving the reverse process allows us to balance the performance and computational speed of the proposed method. Moreover, we show that the proposed method is also suitable for dereverberation and thus not limited to additive background noise removal. Code and audio examples are available online, see https://github.com/sp-uhh/sgmse

翻译：在本研究中，我们延续前期工作，采用基于扩散的生成模型进行语音增强。我们详细阐述了基于随机微分方程的扩散过程，并对其理论意义进行了深入分析。与常规条件生成任务不同，我们并非从纯高斯噪声开始逆向过程，而是从带噪语音与高斯噪声的混合信号出发。这一设计匹配了前向过程——通过引入漂移项，从纯净语音逐步过渡到带噪语音。实验表明，该方法仅需30步扩散即可生成高质量的纯净语音估计。通过改进网络架构，我们显著提升了语音增强性能，这表明原始方法的局限主要在于网络结构而非理论框架。在跨数据集评估中，改进方法可与当前判别式模型相抗衡，且在跨语料库测试中展现出更优的泛化能力。我们辅以真实噪声录音的仪器评估和听力实验，其中所提方法获得最高评分。通过探索不同采样器配置以求解逆向过程，我们实现了性能与计算速度的平衡。此外，该方法同样适用于去混响任务，表明其并不局限于加性背景噪声的去除。代码与音频示例已公开，详见https://github.com/sp-uhh/sgmse。