Hetero-associative memories for voice signal and image processing

Roberto A. Vázquez, Humberto Sossa

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

5 Citas (Scopus)

Resumen

An associative memory AM is a type of neural network commonly used for recalling output patterns from input patterns that might be altered by noise. Most of these models have several constraints that limit their applicability in complex problems. Recently, in [13] a new AM based on some aspects of human brain was introduced, however the authors only test its accuracy using image patterns. In this paper we show that this model is also robust with other type of patterns such as voice signal patterns. The AM is trained with associations composed by voice signals and their corresponding images. Once trained, when a voice signal is used to stimulate the AM we expect the memory recall the image associated to the voice signal. In order to test the accuracy of the proposal, a benchmark of sounds and images was used.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings
Páginas659-666
Número de páginas8
DOI
EstadoPublicada - 2008
Evento13th Iberoamerican Congress on Pattern Recognition, CIARP 2008 - Havana, Cuba
Duración: 9 sep. 200812 sep. 2008

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5197 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th Iberoamerican Congress on Pattern Recognition, CIARP 2008
País/TerritorioCuba
CiudadHavana
Período9/09/0812/09/08

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