Noise reduction using neural lateral inhibition for speech enhancement

Xing, Yannan and Ke, Weijie and Di Caterina, Gaetano and Soraghan, John (2019) Noise reduction using neural lateral inhibition for speech enhancement. International Journal of Machine Learning and Computing. ISSN 2010-3700 (In Press)

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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.

ORCID iDs

Xing, Yannan, Ke, Weijie, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897 and Soraghan, John ORCID logoORCID: https://orcid.org/0000-0003-4418-7391;
CORE (COnnecting REpositories)