Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR-FAC) cochlea models and leaky integrate-and-fire (LIF) neu...

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Autores principales: Xu, Ying, Perera, Samalika, Bethi, Yeshwanth, Afshar, Saeed, van Schaik, André
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151790/
https://www.ncbi.nlm.nih.gov/pubmed/37144092
http://dx.doi.org/10.3389/fnins.2023.1125210
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author Xu, Ying
Perera, Samalika
Bethi, Yeshwanth
Afshar, Saeed
van Schaik, André
author_facet Xu, Ying
Perera, Samalika
Bethi, Yeshwanth
Afshar, Saeed
van Schaik, André
author_sort Xu, Ying
collection PubMed
description This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR-FAC) cochlea models and leaky integrate-and-fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks.
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spelling pubmed-101517902023-05-03 Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model Xu, Ying Perera, Samalika Bethi, Yeshwanth Afshar, Saeed van Schaik, André Front Neurosci Neuroscience This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR-FAC) cochlea models and leaky integrate-and-fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks. Frontiers Media S.A. 2023-04-18 /pmc/articles/PMC10151790/ /pubmed/37144092 http://dx.doi.org/10.3389/fnins.2023.1125210 Text en Copyright © 2023 Xu, Perera, Bethi, Afshar and van Schaik. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Xu, Ying
Perera, Samalika
Bethi, Yeshwanth
Afshar, Saeed
van Schaik, André
Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model
title Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model
title_full Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model
title_fullStr Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model
title_full_unstemmed Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model
title_short Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model
title_sort event-driven spectrotemporal feature extraction and classification using a silicon cochlea model
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151790/
https://www.ncbi.nlm.nih.gov/pubmed/37144092
http://dx.doi.org/10.3389/fnins.2023.1125210
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