Spectogram Prediction with Neural Networks
- Autores
- García, Mario Alejandro; Destéfanis, Eduardo A.
- Año de publicación
- 2018
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- A neural network model for spectrogram magnitude prediction is presented. It has one convolutional layer that computes the shorttime Fourier transform. By choosing the magnitude of the spectrum as output and discarding the phase, it is possible to avoid complex number operations. The structure of the network and coefficients computation for this alternative are presented in detail. The model coefficients can be directly computed or trained with the gradient descent algorithm. In both cases, the results are satisfactory, but the obtained weights are different. An analysis of the differences is made. The main contribution of this article is to show that the proposed model is trainable. Consequently, the coefficients can be adapted to particular problems.
XIX Workshop Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
discrete fourier transform
spectrogram
deep learning
convolutional neural network - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/73033
Ver los metadatos del registro completo
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Spectogram Prediction with Neural NetworksGarcía, Mario AlejandroDestéfanis, Eduardo A.Ciencias Informáticasdiscrete fourier transformspectrogramdeep learningconvolutional neural networkA neural network model for spectrogram magnitude prediction is presented. It has one convolutional layer that computes the shorttime Fourier transform. By choosing the magnitude of the spectrum as output and discarding the phase, it is possible to avoid complex number operations. The structure of the network and coefficients computation for this alternative are presented in detail. The model coefficients can be directly computed or trained with the gradient descent algorithm. In both cases, the results are satisfactory, but the obtained weights are different. An analysis of the differences is made. The main contribution of this article is to show that the proposed model is trainable. Consequently, the coefficients can be adapted to particular problems.XIX Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2018-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf42-51http://sedici.unlp.edu.ar/handle/10915/73033enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-658-472-6info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:12:13Zoai:sedici.unlp.edu.ar:10915/73033Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:12:13.36SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Spectogram Prediction with Neural Networks |
title |
Spectogram Prediction with Neural Networks |
spellingShingle |
Spectogram Prediction with Neural Networks García, Mario Alejandro Ciencias Informáticas discrete fourier transform spectrogram deep learning convolutional neural network |
title_short |
Spectogram Prediction with Neural Networks |
title_full |
Spectogram Prediction with Neural Networks |
title_fullStr |
Spectogram Prediction with Neural Networks |
title_full_unstemmed |
Spectogram Prediction with Neural Networks |
title_sort |
Spectogram Prediction with Neural Networks |
dc.creator.none.fl_str_mv |
García, Mario Alejandro Destéfanis, Eduardo A. |
author |
García, Mario Alejandro |
author_facet |
García, Mario Alejandro Destéfanis, Eduardo A. |
author_role |
author |
author2 |
Destéfanis, Eduardo A. |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas discrete fourier transform spectrogram deep learning convolutional neural network |
topic |
Ciencias Informáticas discrete fourier transform spectrogram deep learning convolutional neural network |
dc.description.none.fl_txt_mv |
A neural network model for spectrogram magnitude prediction is presented. It has one convolutional layer that computes the shorttime Fourier transform. By choosing the magnitude of the spectrum as output and discarding the phase, it is possible to avoid complex number operations. The structure of the network and coefficients computation for this alternative are presented in detail. The model coefficients can be directly computed or trained with the gradient descent algorithm. In both cases, the results are satisfactory, but the obtained weights are different. An analysis of the differences is made. The main contribution of this article is to show that the proposed model is trainable. Consequently, the coefficients can be adapted to particular problems. XIX Workshop Agentes y Sistemas Inteligentes (WASI) Red de Universidades con Carreras en Informática (RedUNCI) |
description |
A neural network model for spectrogram magnitude prediction is presented. It has one convolutional layer that computes the shorttime Fourier transform. By choosing the magnitude of the spectrum as output and discarding the phase, it is possible to avoid complex number operations. The structure of the network and coefficients computation for this alternative are presented in detail. The model coefficients can be directly computed or trained with the gradient descent algorithm. In both cases, the results are satisfactory, but the obtained weights are different. An analysis of the differences is made. The main contribution of this article is to show that the proposed model is trainable. Consequently, the coefficients can be adapted to particular problems. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/73033 |
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http://sedici.unlp.edu.ar/handle/10915/73033 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/isbn/978-950-658-472-6 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 42-51 |
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