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Combining Fully Convolutional and Recurrent Neural Networks for Single Channel Audio Source Separation

Grais, Emad M and Plumbley, Mark (2018) Combining Fully Convolutional and Recurrent Neural Networks for Single Channel Audio Source Separation In: AES 144th Convention, 23 - 26 May 2018, Milan, Italy.

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Abstract

Combining different models is a common strategy to build a good audio source separation system. In this work, we combine two powerful deep neural networks for audio single channel source separation (SCSS). Namely, we combine fully convolutional neural networks (FCNs) and recurrent neural networks, specifically, bidirectional long short-term memory recurrent neural networks (BLSTMs). FCNs are good at extracting useful features from the audio data and BLSTMs are good at modeling the temporal structure of the audio signals. Our experimental results show that combining FCNs and BLSTMs achieves better separation performance than using each model individually.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Grais, Emad Mgrais@surrey.ac.uk
Plumbley, Markm.plumbley@surrey.ac.uk
Date : 2018
Funders : EPSRC
Copyright Disclaimer : Copyright 2018 Audio Engineering Society. All rights reserved.
Depositing User : Melanie Hughes
Date Deposited : 15 Mar 2018 09:23
Last Modified : 16 Mar 2018 10:37
URI: http://epubs.surrey.ac.uk/id/eprint/846033

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