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Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose...

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Detalles Bibliográficos
Autores principales: Bensimon, Moshe, Greenberg, Shlomo, Haiut, Moshe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913968/
https://www.ncbi.nlm.nih.gov/pubmed/33557214
http://dx.doi.org/10.3390/s21041065
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author Bensimon, Moshe
Greenberg, Shlomo
Haiut, Moshe
author_facet Bensimon, Moshe
Greenberg, Shlomo
Haiut, Moshe
author_sort Bensimon, Moshe
collection PubMed
description This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.
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spelling pubmed-79139682021-02-28 Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing Bensimon, Moshe Greenberg, Shlomo Haiut, Moshe Sensors (Basel) Article This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database. MDPI 2021-02-04 /pmc/articles/PMC7913968/ /pubmed/33557214 http://dx.doi.org/10.3390/s21041065 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bensimon, Moshe
Greenberg, Shlomo
Haiut, Moshe
Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
title Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
title_full Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
title_fullStr Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
title_full_unstemmed Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
title_short Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
title_sort using a low-power spiking continuous time neuron (sctn) for sound signal processing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913968/
https://www.ncbi.nlm.nih.gov/pubmed/33557214
http://dx.doi.org/10.3390/s21041065
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