A perceptually-weighted deep neural network for monaural speech enhancement in various background noise conditions

Liu, Qingju, Wang, Wenwu, Jackson, Philip JB and Tang, Y 2017, A perceptually-weighted deep neural network for monaural speech enhancement in various background noise conditions , in: EUSIPCO 2017, the 25th European Signal Processing Conference, 28th August - 2nd September 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 (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: EUSIPCO 2017, the 25th European Signal Processing Conference
Funders: Engineering and Physical Sciences Research Council (EPSRC)
Depositing User: Y Tang
Date Deposited: 01 Jun 2017 15:13
Last Modified: 08 Aug 2017 12:19
URI: http://usir.salford.ac.uk/id/eprint/42454

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