NAVIS: Neuromorphic Auditory VISualizer Tool

This software presents diverse utilities to perform the first post-processing layer taking the neuromorphic auditory sensors (NAS) information. The used NAS implements in FPGA a cascade filters architecture, imitating the behavior of the basilar membrane and inner hair cells and working with the sou...

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Detalhes bibliográficos
Autores: Domínguez Morales, Juan Pedro, Jiménez Fernández, Ángel Francisco, Domínguez Morales, Manuel Jesús, Jiménez Moreno, Gabriel
Tipo de documento: artigo
Estado:Versión enviada para evaluación y publicación
Data de publicação:2017
País:España
Recursos:Universidad de Sevilla (US)
Repositório:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/91428
Acesso em linha:https://hdl.handle.net/11441/91428
https://doi.org/10.1016/j.neucom.2016.12.046
Access Level:Acceso aberto
Palavra-chave:Neuromorphic engineering
Spiking neural networks
Address-event-representation
neuro-inspired auditory sensor
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spelling NAVIS: Neuromorphic Auditory VISualizer ToolDomínguez Morales, Juan PedroJiménez Fernández, Ángel FranciscoDomínguez Morales, Manuel JesúsJiménez Moreno, GabrielNeuromorphic engineeringSpiking neural networksAddress-event-representationneuro-inspired auditory sensorThis software presents diverse utilities to perform the first post-processing layer taking the neuromorphic auditory sensors (NAS) information. The used NAS implements in FPGA a cascade filters architecture, imitating the behavior of the basilar membrane and inner hair cells and working with the sound information decomposed into its frequency components as spike streams. The well-known neuromorphic hardware interface Address-Event-Representation (AER) is used to propagate auditory information out of the NAS, emulating the auditory vestibular nerve. Using the information packetized into aedat files, which are generated through the jAER software plus an AER to USB computer interface, NAVIS implements a set of graphs that allows to represent the auditory information as cochleograms, histograms, sonograms, etc. It can also split the auditory information into different sets depending on the activity level of the spike streams. The main contribution of this software tool is that it allows complex audio post-processing treatments and representations, which is a novelty for spike-based systems in the neuromorphic community and it will help neuromorphic engineers to build sets for training spiking neural networks (SNN).Ministerio de Economía y Competitividad TEC2012-37868-C04-02ElsevierArquitectura y Tecnología de Computadores2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/91428https://doi.org/10.1016/j.neucom.2016.12.046reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésNeurocomputing, 237 (may 2017), 418-422.TEC2012-37868-C04-02https://www.sciencedirect.com/science/article/pii/S0925231216315624info:eu-repo/semantics/openAccessoai:idus.us.es:11441/914282026-06-17T12:51:07Z
dc.title.none.fl_str_mv NAVIS: Neuromorphic Auditory VISualizer Tool
title NAVIS: Neuromorphic Auditory VISualizer Tool
spellingShingle NAVIS: Neuromorphic Auditory VISualizer Tool
Domínguez Morales, Juan Pedro
Neuromorphic engineering
Spiking neural networks
Address-event-representation
neuro-inspired auditory sensor
title_short NAVIS: Neuromorphic Auditory VISualizer Tool
title_full NAVIS: Neuromorphic Auditory VISualizer Tool
title_fullStr NAVIS: Neuromorphic Auditory VISualizer Tool
title_full_unstemmed NAVIS: Neuromorphic Auditory VISualizer Tool
title_sort NAVIS: Neuromorphic Auditory VISualizer Tool
dc.creator.none.fl_str_mv Domínguez Morales, Juan Pedro
Jiménez Fernández, Ángel Francisco
Domínguez Morales, Manuel Jesús
Jiménez Moreno, Gabriel
author Domínguez Morales, Juan Pedro
author_facet Domínguez Morales, Juan Pedro
Jiménez Fernández, Ángel Francisco
Domínguez Morales, Manuel Jesús
Jiménez Moreno, Gabriel
author_role author
author2 Jiménez Fernández, Ángel Francisco
Domínguez Morales, Manuel Jesús
Jiménez Moreno, Gabriel
author2_role author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
dc.subject.none.fl_str_mv Neuromorphic engineering
Spiking neural networks
Address-event-representation
neuro-inspired auditory sensor
topic Neuromorphic engineering
Spiking neural networks
Address-event-representation
neuro-inspired auditory sensor
description This software presents diverse utilities to perform the first post-processing layer taking the neuromorphic auditory sensors (NAS) information. The used NAS implements in FPGA a cascade filters architecture, imitating the behavior of the basilar membrane and inner hair cells and working with the sound information decomposed into its frequency components as spike streams. The well-known neuromorphic hardware interface Address-Event-Representation (AER) is used to propagate auditory information out of the NAS, emulating the auditory vestibular nerve. Using the information packetized into aedat files, which are generated through the jAER software plus an AER to USB computer interface, NAVIS implements a set of graphs that allows to represent the auditory information as cochleograms, histograms, sonograms, etc. It can also split the auditory information into different sets depending on the activity level of the spike streams. The main contribution of this software tool is that it allows complex audio post-processing treatments and representations, which is a novelty for spike-based systems in the neuromorphic community and it will help neuromorphic engineers to build sets for training spiking neural networks (SNN).
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/91428
https://doi.org/10.1016/j.neucom.2016.12.046
url https://hdl.handle.net/11441/91428
https://doi.org/10.1016/j.neucom.2016.12.046
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Neurocomputing, 237 (may 2017), 418-422.
TEC2012-37868-C04-02
https://www.sciencedirect.com/science/article/pii/S0925231216315624
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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