Single-Channel Signal Separation and Deconvolution
with Generative Adversarial Networks
Abstract
Single-channel signal separation and deconvolution
aims to separate and deconvolve individual sources
from a single-channel mixture and is a challenging problem in which no prior knowledge of the
mixing filters is available. Both individual sources
and mixing filters need to be estimated. In addition, a mixture may contain non-stationary noise
which is unseen in the training set. We propose
a synthesizing-decomposition (S-D) approach to
solve the single-channel separation and deconvolution problem. In synthesizing, a generative model
for sources is built using a generative adversarial network (GAN). In decomposition, both mixing filters and sources are optimized to minimize
the reconstruction error of the mixture. The proposed S-D approach achieves a peak-to-noise-ratio
(PSNR) of 18.9 dB and 15.4 dB in image inpainting
and completion, outperforming a baseline convolutional neural network PSNR of 15.3 dB and 12.2
dB, respectively and achieves a PSNR of 13.2 dB in
source separation together with deconvolution, outperforming a convolutive non-negative matrix factorization (NMF) baseline of 10.1 dB.