Recurrent lateral inhibitory spiking networks for speech enhancement

Conference paper

Wall, J., Glackin, Cornelius, Cannings, Nigel, Chollet, Gerard and Dugan, Nazim 2016. Recurrent lateral inhibitory spiking networks for speech enhancement. IEEE International Joint Conference on Neural Networks (IJCNN). Vancouver, Canada 24 - 29 Jul 2016 IEEE. pp. 1023-1028 https://doi.org/10.1109/IJCNN.2016.7727310
AuthorsWall, J., Glackin, Cornelius, Cannings, Nigel, Chollet, Gerard and Dugan, Nazim
TypeConference paper
Abstract

Automatic speech recognition accuracy is affected adversely by the presence of noise. In this paper we present a novel noise removal and speech enhancement technique based on spiking neural network processing of speech data. The spiking network has a recurrent lateral topology that is biologically inspired, specifically by the inhibitory cells of the cochlear nucleus. The network can be configured for different acoustic environments and it will be demonstrated how the connectivity results in enhancement of temporal correlation between similar frequency bands and removal of uncorrelated noise sources. Demonstration of the speech enhancement capability will be provided with data taken from the TIMIT database with different levels of additive Gaussian white noise. Future directions for further development of this novel approach to noise removal and signal processing will also be discussed.

Keywordsspiking neural networks; noise reduction; speech; lateral inhibition
Year2016
ConferenceIEEE International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Accepted author manuscript
License
CC BY-NC-ND
File Access Level
Anyone
Publication dates
Online03 Nov 2016
Publication process dates
Deposited21 Nov 2016
Accepted24 Jul 2016
ISSN2161-4407
Book titleProceedings of 2016 International Joint Conference on Neural Networks (IJCNN)
ISBN978-1-5090-0620-5
Digital Object Identifier (DOI)https://doi.org/10.1109/IJCNN.2016.7727310
Web address (URL)http://dx.doi/10.1109/IJCNN.2016.7727310
Additional information

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Page range1023-1028
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