Title: Semi-blind source separation with multichannel variational autoencoder,
Abstract: 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.