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Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks

Kong, Qiuqiang, Xu, Yong, Jackson, Philip J.B., Wang, Wenwu and Plumbley, Mark D. (2019) Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks In: IJCAI-19, Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019-08-10-2019-08-16, Macao, China.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
NameEmailORCID
Kong, Qiuqiangq.kong@surrey.ac.uk
Xu, Yong
Jackson, Philip J.B.pj0020@surrey.ac.uk
Wang, WenwuW.Wang@surrey.ac.uk
Plumbley, Mark D.m.plumbley@surrey.ac.uk
Editors :
NameEmailORCID
Kraus, Sarit
Date : August 2019
Funders : EPSRC - Engineering and Physical Sciences Research Council
DOI : 10.24963/ijcai.2019/381
Copyright Disclaimer : Copyright © 2019 International Joint Conferences on Artificial Intelligence All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
Uncontrolled Keywords : Machine Learning: Deep Learning; Machine Learning: Learning Generative Models
Related URLs :
Depositing User : Diane Maxfield
Date Deposited : 22 Aug 2019 13:24
Last Modified : 22 Aug 2019 13:24
URI: http://epubs.surrey.ac.uk/id/eprint/852463

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