Power Cepstrum Calculation with Convolutional Neural Networks : Cálculo del power cepstrum con redes neuronales convolucionales

A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. Although the propo...

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Detalles Bibliográficos
Autores principales:	García, Mario Alejandro, Destéfanis, Eduardo Atilio
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2019
Materias:	Ciencias Informáticas Cepstrum Discrete Fourier transform Spectrogram Deep learning Convolutional neural network Transformada discreta de Fourier Espectrograma Aprendizaje profundo Red neuronal convolucional
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/87765
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-87765
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas Cepstrum Discrete Fourier transform Spectrogram Deep learning Convolutional neural network Transformada discreta de Fourier Espectrograma Aprendizaje profundo Red neuronal convolucional
spellingShingle	Ciencias Informáticas Cepstrum Discrete Fourier transform Spectrogram Deep learning Convolutional neural network Transformada discreta de Fourier Espectrograma Aprendizaje profundo Red neuronal convolucional García, Mario Alejandro Destéfanis, Eduardo Atilio Power Cepstrum Calculation with Convolutional Neural Networks : Cálculo del power cepstrum con redes neuronales convolucionales
topic_facet	Ciencias Informáticas Cepstrum Discrete Fourier transform Spectrogram Deep learning Convolutional neural network Transformada discreta de Fourier Espectrograma Aprendizaje profundo Red neuronal convolucional
description	A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. Although the proposed neural networks weights can be calculated in a direct way, it is necessary to determine if they can be obtained through training with the gradient descent method. In order to analyse the training behaviour, tests are made on the proposed model, as well as on two variants (power spectrum and autocovariance). Results show that the calculation model of power cepstrum cannot be trained, but the analysed variants in fact can.
format	Articulo Articulo
author	García, Mario Alejandro Destéfanis, Eduardo Atilio
author_facet	García, Mario Alejandro Destéfanis, Eduardo Atilio
author_sort	García, Mario Alejandro
title	Power Cepstrum Calculation with Convolutional Neural Networks : Cálculo del power cepstrum con redes neuronales convolucionales
title_short	Power Cepstrum Calculation with Convolutional Neural Networks : Cálculo del power cepstrum con redes neuronales convolucionales
title_full	Power Cepstrum Calculation with Convolutional Neural Networks : Cálculo del power cepstrum con redes neuronales convolucionales
title_fullStr	Power Cepstrum Calculation with Convolutional Neural Networks : Cálculo del power cepstrum con redes neuronales convolucionales
title_full_unstemmed	Power Cepstrum Calculation with Convolutional Neural Networks : Cálculo del power cepstrum con redes neuronales convolucionales
title_sort	power cepstrum calculation with convolutional neural networks : cálculo del power cepstrum con redes neuronales convolucionales
publishDate	2019
url	http://sedici.unlp.edu.ar/handle/10915/87765
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bdutipo_str	Repositorios
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Power Cepstrum Calculation with Convolutional Neural Networks : Cálculo del power cepstrum con redes neuronales convolucionales

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