University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Discriminative Enhancement for Single Channel Audio Source Separation using Deep Neural Networks

Grais, EM, Roma, G, Simpson, AJR and Plumbley, Mark (2017) Discriminative Enhancement for Single Channel Audio Source Separation using Deep Neural Networks In: 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), 2017-02-21 - 2017-02-23, Grenoble, France.

[img]
Preview
Text
final_lva_ica2017.pdf - Accepted version Manuscript
Available under License : See the attached licence file.

Download (223kB) | Preview
[img]
Preview
Text (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

Abstract

The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To tackle this problem, we propose to enhance the separated sources to decrease the distortion and interference between the separated sources using deep neural networks (DNNs). Two different DNNs are used in this work. The first DNN is used to separate the sources from the mixed signal. The second DNN is used to enhance the separated signals. To consider the interactions between the separated sources, we propose to use a single DNN to enhance all the separated sources together. To reduce the residual signals of one source from the other separated sources (interference), we train the DNN for enhancement discriminatively to maximize the dissimilarity between the predicted sources. The experimental results show that using discriminative enhancement decreases the distortion and interference between the separated sources

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
NameEmailORCID
Grais, EMUNSPECIFIEDUNSPECIFIED
Roma, GUNSPECIFIEDUNSPECIFIED
Simpson, AJRUNSPECIFIEDUNSPECIFIED
Plumbley, Markm.plumbley@surrey.ac.ukUNSPECIFIED
Date : 15 February 2017
Identification Number : 10.1007/978-3-319-53547-0_23
Copyright Disclaimer : The final publication is available at link.springer.com
Contributors :
ContributionNameEmailORCID
UNSPECIFIEDSpringer, UNSPECIFIEDUNSPECIFIED
Related URLs :
Additional Information : Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)
Depositing User : Symplectic Elements
Date Deposited : 14 Dec 2016 15:09
Last Modified : 07 Jul 2017 11:37
URI: http://epubs.surrey.ac.uk/id/eprint/813112

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800