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Deep neural network based audio source separation

Zermini, Alfredo, Yu, Yang, Xu, Yong, Plumbley, Mark and Wang, Wenwu (2016) Deep neural network based audio source separation In: 11th IMA International Conference on Mathematics in Signal Processing, 2016-12-12 - 2016-12-14, IET Austin Court, Birmingham, UK.

Deep neural network based audio source separation.pdf - Accepted version Manuscript

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Audio source separation aims to extract individual sources from mixtures of multiple sound sources. Many techniques have been developed such as independent compo- nent analysis, computational auditory scene analysis, and non-negative matrix factorisa- tion. A method based on Deep Neural Networks (DNNs) and time-frequency (T-F) mask- ing has been recently developed for binaural audio source separation. In this method, the DNNs are used to predict the Direction Of Arrival (DOA) of the audio sources with respect to the listener which is then used to generate soft T-F masks for the recovery/estimation of the individual audio sources.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Yu, Yang
Date : 14 December 2016
Copyright Disclaimer : © 2017 The Authors.
Depositing User : Clive Harris
Date Deposited : 11 Aug 2017 08:19
Last Modified : 25 Jun 2018 14:32

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