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Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation

Grais, Emad M, Wierstorf, Hagen, Ward, Dominic and Plumbley, Mark D (2018) Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation In: LVA/ICA 2018: 14th International Conference on Latent Variable Analysis and Signal Separation, July 2-6, 2018, University of Surrey, Guildford, UK.

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In deep neural networks with convolutional layers, all the neurons in each layer typically have the same size receptive fields (RFs) with the same resolution. Convolutional layers with neurons that have large RF capture global information from the input features, while layers with neurons that have small RF size capture local details with high resolution from the input features. In this work, we introduce novel deep multi-resolution fully convolutional neural networks (MR-FCN), where each layer has a range of neurons with different RF sizes to extract multi- resolution features that capture the global and local information from its input features. The proposed MR-FCN is applied to separate the singing voice from mixtures of music sources. Experimental results show that using MR-FCN improves the performance compared to feedforward deep neural networks (DNNs) and single resolution deep fully convolutional neural networks (FCNs) on the audio source separation problem.

Item Type: Conference or Workshop Item (Conference Poster)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Grais, Emad
Plumbley, Mark
Date : 6 June 2018
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1007/978-3-319-93764-9_32
Copyright Disclaimer : Copyright 2018 Springer Verlag. The final authenticated version is available online at
Uncontrolled Keywords : Multi-resolution features extraction, fully convolutional neu- ral networks, deep learning, audio source separation, audio enhancement.
Related URLs :
Depositing User : Melanie Hughes
Date Deposited : 27 Apr 2018 08:33
Last Modified : 11 Dec 2018 11:24

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