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IJMLC 2021 Vol.11(5): 357-361 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.5.1061

Noise Reduction Using Neural Lateral Inhibition for Speech Enhancement

Yannan Xing, Weijie Ke, Gaetano Di Caterina, and John Soraghan

Abstract—Recurrent spiking neurons with lateral inhibition connection play a vital role in human’s brain functional abilities. In this paper, we propose a novel noise reduction method that is based on neuron rate coding and bio-inspired spiking neural network architecture. The excitatory-inhibitory topology in the network acts as the temporal characteristic synchrony and coincidence detector that removes uncorrelated noisy spikes. A LIF source encoder is introduced along with the network. The network uses generated binary Short-Time Fourier Transform (STFT) masks according to the rate of processed spike train, which is used to reconstruct the denoised speech signal. The technique is evaluated on noisy speech samples with 5 types of real-world additive noise with different noise strength.

Index Terms—Spiking neural network, speech enhancement, noise reduction, lateral inhibition.

Yannan Xing, Weijie Ke, Gaetano Di Caterina, and John Soraghan are with the Deep Learning and Neuromorphic Lab, Centre for Image and Signal Processing, Electronics and Electrical Engineering Department University of Strathclyde, Glasgow, UK (e-mail: yannan.xing@ strath.ac.uk).

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Cite: Yannan Xing, Weijie Ke, Gaetano Di Caterina, and John Soraghan, "Noise Reduction Using Neural Lateral Inhibition for Speech Enhancement," International Journal of Machine Learning and Computing vol. 11, no. 5, pp. 357-361, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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