Recurrent lateral inhibitory spiking networks for speech enhancement

Book chapter


Wall, J., Glackin, Cornelius, Cannings, Nigel, Chollet, Gerard and Dugan, Nazim 2016. Recurrent lateral inhibitory spiking networks for speech enhancement. in: Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN) IEEE. pp. 1023-1028
AuthorsWall, J., Glackin, Cornelius, Cannings, Nigel, Chollet, Gerard and Dugan, Nazim
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
Book titleProceedings of 2016 International Joint Conference on Neural Networks (IJCNN)
Page range1023-1028
Year2016
PublisherIEEE
Publication dates
Print03 Nov 2016
Publication process dates
Deposited21 Nov 2016
Accepted24 Jul 2016
EventIEEE International Joint Conference on Neural Networks (IJCNN)
ISBN978-1-5090-0620-5
2161-4407
Digital Object Identifier (DOI)doi:10.1109/IJCNN.2016.7727310
Web address (URL)http://dx.doi/10.1109/IJCNN.2016.7727310
https://www.researchgate.net/publication/301356222_Recurrent_lateral_inhibitory_spiking_networks_for_speech_enhancement
Additional information

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Accepted author manuscript
License
CC BY-NC-ND
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