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Combining Mask Estimates for Single Channel Audio Source Separation using Deep Neural Networks

Girgis, EMG, Roma, G, Simpson, AJR and Plumbley, MD (2016) Combining Mask Estimates for Single Channel Audio Source Separation using Deep Neural Networks In: Interspeech2016, 2016-09-08 - 2016-09-12, San Francisco, California.

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Abstract

Deep neural networks (DNNs) are usually used for single channel source separation to predict either soft or binary time frequency masks. The masks are used to separate the sources from the mixed signal. Binary masks produce separated sources with more distortion and less interference than soft masks. In this paper, we propose to use another DNN to combine the estimates of binary and soft masks to achieve the advantages and avoid the disadvantages of using each mask individually. We aim to achieve separated sources with low distortion and low interference between each other. Our experimental results show that combining the estimates of binary and soft masks using DNN achieves lower distortion than using each estimate individually and achieves as low interference as the binary mask.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
AuthorsEmailORCID
Girgis, EMGUNSPECIFIEDUNSPECIFIED
Roma, GUNSPECIFIEDUNSPECIFIED
Simpson, AJRUNSPECIFIEDUNSPECIFIED
Plumbley, MDUNSPECIFIEDUNSPECIFIED
Date : September 2016
Copyright Disclaimer : Copyright 2016 ISCA
Contributors :
ContributionNameEmailORCID
UNSPECIFIEDISCA, UNSPECIFIEDUNSPECIFIED
Uncontrolled Keywords : : Combining estimates, deep neural networks, single channel source separation, neural network ensembles, deep learning
Depositing User : Symplectic Elements
Date Deposited : 01 Jul 2016 15:18
Last Modified : 01 Jul 2016 15:18
URI: http://epubs.surrey.ac.uk/id/eprint/811087

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