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A Perceptually-Weighted Deep Neural Network for Monaural Speech Enhancement in Various Background Noise Conditions

Liu, Qingju, Wang, Wenwu, Jackson, Philip and Tang, Yan (2017) A Perceptually-Weighted Deep Neural Network for Monaural Speech Enhancement in Various Background Noise Conditions In: 2017 25th European Signal Processing Conference (EUSIPCO), 28 Aug - 2 Sep 2017, Kos Island, Greece.

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Abstract

Deep neural networks (DNN) have recently been shown to give state-of-the-art performance in monaural speech enhancement. However in the DNN training process, the perceptual difference between different components of the DNN output is not fully exploited, where equal importance is often assumed. To address this limitation, we have proposed a new perceptually-weighted objective function within a feedforward DNN framework, aiming to minimize the perceptual difference between the enhanced speech and the target speech. A perceptual weight is integrated into the proposed objective function, and has been tested on two types of output features: spectra and ideal ratio masks. Objective evaluations for both speech quality and speech intelligibility have been performed. Integration of our perceptual weight shows consistent improvement on several noise levels and a variety of different noise types.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Liu, Qingjuq.liu@surrey.ac.ukUNSPECIFIED
Wang, WenwuW.Wang@surrey.ac.ukUNSPECIFIED
Jackson, PhilipP.Jackson@surrey.ac.ukUNSPECIFIED
Tang, YanUNSPECIFIEDUNSPECIFIED
Date : 2 September 2017
Funders : Engineering and Physical Sciences Research Council (EPSRC)
Grant Title : Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home
Copyright Disclaimer : © EURASIP 2017
Depositing User : Clive Harris
Date Deposited : 26 Jul 2017 13:05
Last Modified : 26 Jul 2017 13:05
URI: http://epubs.surrey.ac.uk/id/eprint/841759

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