In this paper, we address the unsupervised speech enhancement problem based
on recurrent variational autoencoder (RVAE). This approach offers promising
generalization performance over the supervised counterpart. Nevertheless, the
involved iterative variational expectation-maximization (VEM) process at test
time, which relies on a variational inference method, results in high
computational complexity. To tackle this issue, we present efficient sampling
techniques based on Langevin dynamics and Metropolis-Hasting algorithms,
adapted to the EM-based speech enhancement with RVAE. By directly sampling from
the intractable posterior distribution within the EM process, we circumvent the
intricacies of variational inference. We conduct a series of experiments,
comparing the proposed methods with VEM and a state-of-the-art supervised
speech enhancement approach based on diffusion models. The results reveal that
our sampling-based algorithms significantly outperform VEM, not only in terms
of computational efficiency but also in overall performance. Furthermore, when
compared to the supervised baseline, our methods showcase robust generalization
performance in mismatched test conditions.

本文中，我们提出了基于循环变分自动编码器（RVAE）的无监督语音增强问题。我们使用高效的采样技术解决了测试时间中涉及的计算复杂度问题，并通过与传统方法进行比较实验证明了这种方法在计算效率和总体性能方面的显著优势。

无监督语音增强的后验采样算法与递归变分自动编码器

Posterior sampling algorithms for unsupervised speech enhancement with  recurrent variational autoencoder

This work builds on a previous work on unsupervised speech enhancement using
a dynamical variational autoencoder (DVAE) as the clean speech model and
non-negative matrix factorization (NMF) as the noise model. We propose to
replace the NMF noise model with a deep dynamical generative model (DDGM)
depending either on the DVAE latent variables, or on the noisy observations, or
on both. This DDGM can be trained in three configurations: noise-agnostic,
noise-dependent and noise adaptation after noise-dependent training.
Experimental results show that the proposed method achieves competitive
performance compared to state-of-the-art unsupervised speech enhancement
methods, while the noise-dependent training configuration yields a much more
time-efficient inference process.

使用深度动态生成模型和动态变分自动编码器代替非负矩阵分解模型，进行语音增强的无监督学习方法获得了与最先进的方法相竞争的性能，而噪声依赖性训练配置使推理过程效率更高。