Fast and efficient speech enhancement with variational autoencoders. (arXiv:2211.02728v1 [cs.SD])

Unsupervised speech enhancement based on variational autoencoders has shown
promising performance compared with the commonly used supervised methods. This
approach involves the use of a pre-trained deep speech prior along with a
parametric noise model, where the noise parameters are learned from the noisy
speech signal with an expectationmaximization (EM)-based method. The E-step
involves an intractable latent posterior distribution. Existing algorithms to
solve this step are either based on computationally heavy Monte Carlo Markov
Chain sampling methods and variational inference, …

arxiv speech variational autoencoders