Abstract
Training neural networks for source separation involves presenting a mixture recording at the input of the network and updating network parameters in order to produce an output that resembles the clean source. Consequently, supervised source separation depends on the availability of paired mixture-clean training examples. In this paper, we interpret source separation as a style transfer problem. We present a variational auto-encoder network that exploits the commonality across the domain of mixtures and the domain of clean sounds and learns a shared latent representation across the two domains. Using these cycle-consistent variational auto-encoders, we learn a mapping from the mixture domain to the domain of clean sounds and perform source separation without explicitly supervising with paired training examples.
Abstract (translated)
训练神经网络进行源分离，包括在网络输入端呈现混合记录，并更新网络参数，以产生类似于清洁源的输出。因此，监督源分离取决于成对混合物清洁培训示例的可用性。在本文中，我们将源分离解释为一个样式转换问题。我们提出了一个变分的自动编码网络，它利用了混合和干净声音领域的共性，并学习了两个领域的共享潜在表示。使用这些循环一致的变分自动编码器，我们学习了从混合域到干净声音域的映射，并且在不显式监督配对训练示例的情况下执行源分离。
URL
https://arxiv.org/abs/1905.00151
