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Single Channel Audio Source Separation using Convolutional Denoising Autoencoders

Grais, Emad M and Plumbley, Mark D (2017) Single Channel Audio Source Separation using Convolutional Denoising Autoencoders In: 5th IEEE Global Conference on Signal and Information Processing (GlobalSIP2017), 14 - 16 November 2017, Montreal, Canada.

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Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed signal. Each CDAE is trained to separate one source and treats the other sources as background noise. The main idea is to allow each CDAE to learn suitable spectral-temporal filters and features to its corresponding source. Our experimental results show that CDAEs perform source separation slightly better than the deep feedforward neural networks (FNNs) even with fewer parameters than FNNs.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Grais, Emad
Plumbley, Mark
Date : 16 November 2017
DOI : 10.1109/GlobalSIP.2017.8309164
Copyright Disclaimer : Copyright 2017 IEEE. Published in the IEEE 2017 Global Conference on Signal and Information Processing (GlobalSIP 2017), scheduled for November 14-16, 2017 in Montreal, Canada. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966.
Uncontrolled Keywords : Fully convolutional denoising autoencoders, single channel audio source separation, stacked convolutional autoencoders, deep convolutional neural networks, deep learning.
Related URLs :
Depositing User : Melanie Hughes
Date Deposited : 09 Aug 2017 07:58
Last Modified : 07 Aug 2018 08:24

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