标题： 基于多通道变分自编码器的半盲源分离 显示英文标题

标题： Semi-blind source separation with multichannel variational autoencoder

摘要： 本文提出了一种多通道源分离技术，称为多通道变分自编码器（MVAE）方法，该方法使用条件变分自编码器（CVAE）来建模和估计混合中的源的功率谱图。通过使用带有源类别标签的训练样本的谱图来训练CVAE，我们可以将训练好的解码器分布用作通用生成模型，能够根据指定的类别标签生成谱图。通过将潜在空间变量和类别标签视为该生成模型的未知参数，我们可以开发出一种收敛保证的半盲源分离算法，该算法包括迭代估计底层源的功率谱图以及分离矩阵。在实验评估中，我们的MVAE比基线方法产生了更好的分离性能。 显示英文摘要

摘要： This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class labels, we can use the trained decoder distribution as a universal generative model capable of generating spectrograms conditioned on a specified class label. By treating the latent space variables and the class label as the unknown parameters of this generative model, we can develop a convergence-guaranteed semi-blind source separation algorithm that consists of iteratively estimating the power spectrograms of the underlying sources as well as the separation matrices. In experimental evaluations, our MVAE produced better separation performance than a baseline method.