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Binaural and Log-Power Spectra Features with Deep Neural Networks for Speech-Noise Separation

Zermini, Alfredo, Liu, Qingju, Xu, Yong, Plumbley, Mark, Betts, Dave and Wang, Wenwu (2017) Binaural and Log-Power Spectra Features with Deep Neural Networks for Speech-Noise Separation In: MMSP 2017 - IEEE 19th International Workshop on Multimedia Signal Processing, 16-18 Oct 2017, Putteridge Bury, Luton, Bedfordshire, UK.

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Binaural and Log-Power Spectra Features with Deep Neural Networks for Speech-Noise Separation.pdf - Accepted version Manuscript
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Abstract

Binaural features of interaural level difference and interaural phase difference have proved to be very effective in training deep neural networks (DNNs), to generate timefrequency masks for target speech extraction in speech-speech mixtures. However, effectiveness of binaural features is reduced in more common speech-noise scenarios, since the noise may over-shadow the speech in adverse conditions. In addition, the reverberation also decreases the sparsity of binaural features and therefore adds difficulties to the separation task. To address the above limitations, we highlight the spectral difference between speech and noise spectra and incorporate the log-power spectra features to extend the DNN input. Tested on two different reverberant rooms at different signal to noise ratios (SNR), our proposed method shows advantages over the baseline method using only binaural features in terms of signal to distortion ratio (SDR) and Short-Time Perceptual Intelligibility (STOI).

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Zermini, Alfredoalfredo.zermini@surrey.ac.ukUNSPECIFIED
Liu, Qingjuq.liu@surrey.ac.ukUNSPECIFIED
Xu, Yongyong.xu@surrey.ac.ukUNSPECIFIED
Plumbley, Markm.plumbley@surrey.ac.ukUNSPECIFIED
Betts, DaveUNSPECIFIEDUNSPECIFIED
Wang, WenwuW.Wang@surrey.ac.ukUNSPECIFIED
Date : 18 October 2017
Copyright Disclaimer : © 2017 IEEE
Depositing User : Clive Harris
Date Deposited : 11 Aug 2017 13:11
Last Modified : 11 Aug 2017 13:11
URI: http://epubs.surrey.ac.uk/id/eprint/841892

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