dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Liu, Qian | es |
dc.creator | James, Robert | es |
dc.creator | Gutiérrez Galán, Daniel | es |
dc.creator | Jiménez Fernández, Ángel Francisco | es |
dc.creator | Davidson, Simón | es |
dc.creator | Furber, Steve B. | es |
dc.date.accessioned | 2020-01-22T11:35:35Z | |
dc.date.available | 2020-01-22T11:35:35Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Domínguez Morales, J.P., Liu, Q., James, R., Gutiérrez Galán, D., Jiménez Fernández, Á.F., Davidson, S. y Furber, S. B. (2018). Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach. En IJCNN 2018 : International Joint Conference on Neural Networks Rio de Janeiro, Brazil: IEEE Computer Society. | |
dc.identifier.isbn | 978-1-5090-6014-6 | es |
dc.identifier.issn | 2161-4407 | es |
dc.identifier.uri | https://hdl.handle.net/11441/92113 | |
dc.description.abstract | Speech recognition has become an important task
to improve the human-machine interface. Taking into account
the limitations of current automatic speech recognition systems,
like non-real time cloud-based solutions or power demand,
recent interest for neural networks and bio-inspired systems has
motivated the implementation of new techniques.
Among them, a combination of spiking neural networks and
neuromorphic auditory sensors offer an alternative to carry
out the human-like speech processing task. In this approach,
a spiking convolutional neural network model was implemented,
in which the weights of connections were calculated by training
a convolutional neural network with specific activation functions,
using firing rate-based static images with the spiking information
obtained from a neuromorphic cochlea.
The system was trained and tested with a large dataset
that contains ”left” and ”right” speech commands, achieving
89.90% accuracy. A novel spiking neural network model has been
proposed to adapt the network that has been trained with static
images to a non-static processing approach, making it possible
to classify audio signals and time series in real time. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TEC2016-77785-P | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IJCNN 2018 : International Joint Conference on Neural Networks (2018), | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Speech recognition | es |
dc.subject | Audio processing | es |
dc.subject | Spiking neural network | es |
dc.subject | Convolutional Neural Networks (CNN) | es |
dc.subject | Neuromorphic hardware | es |
dc.subject | Deep learning | es |
dc.title | Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | TEC2016-77785-P | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8489381 | es |
dc.identifier.doi | 10.1109/IJCNN.2018.8489381 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación | es |
idus.format.extent | 8 | es |
dc.eventtitle | IJCNN 2018 : International Joint Conference on Neural Networks | es |
dc.eventinstitution | Rio de Janeiro, Brazil | es |
dc.relation.publicationplace | New York, USA | es |