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Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders

Grais, Emad M, Ward, Dominic and Plumbley, Mark D (2018) Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders In: 2018 26th European Signal Processing Conference (EUSIPCO), 3 - 7 September 2018, Rome, Italy.

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Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. In this work, we introduce a novel multi-channel, multiresolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multiresolution features for separating the singing-voice from stereo music. Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Grais, Emad
Plumbley, Mark
Date : 3 December 2018
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.23919/EUSIPCO.2018.8553571
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User : Melanie Hughes
Date Deposited : 28 Jun 2018 09:16
Last Modified : 11 Dec 2018 11:24

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