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Titel
Sistema híbrido inteligente para el análisis de contenido multimedia
Autor(es)
Director(es)
Schlagwort
Tesis y disertaciones académicas
Universidad de Salamanca (España)
Tesis Doctoral
Academic dissertations
TIC
Inteligencia artificial
Internet de las cosas
Arquitectura de ordenadores
Clasificación UNESCO
1203.04 Inteligencia Artificial
3304.06 Arquitectura de Ordenadores
Fecha de publicación
2020
Resumen
[ES] Los grandes avances en las áreas de las TIC, el IoT y la IA han propiciado
una serie de sistemas cuyo uso se ha visto incrementado exponencialmente
en los últimos años, fomentando la generación de ingentes cantidades de datos
de naturaleza heterogénea. Las propuestas recogidas en la literatura para
la explotación de estos datos están enfocadas a la resolución de problemas
muy específicos, favoreciendo el desaprovechamiento de la información. Este
trabajo plantea una arquitectura modular y flexible para implementar un
sistema híbrido inteligente capaz de soportar diferentes procesos de análisis
de contenido multimedia gracias a la adaptación del concepto de ETL y la
aplicación de tuberías de datos. Con el objetivo de comprobar el potencial
de la arquitectura propuesta, se diseñan dos frameworks para la automatización
del proceso de composición musical descriptiva a partir de contenido
audiovisual y se desarrollan dos casos de estudio bien diferenciados donde
se aplican diversas técnicas de extracción de meta-información y algoritmos
enmarcados en el área del aprendizaje automático. La discusión de los resultados
obtenidos se realiza considerando el rendimiento de los algoritmos
y la aceptación social de la música por medio de diferentes test de usuario.
En conclusión, la propuesta favorece la validación de la hipótesis previamente
establecida, evidenciando que los datos multimedia analizados mediante
técnicas de IA permiten crear otro tipo de información útil para el usuario.
[EN] Great advances in the fields of Information and Communication Technologies,
IoT and AI have led to a series of systems whose use has increased
exponentially over the past few years. This has encouraged the generation
of huge amounts of data of a heterogeneous nature. The state of the art
gathers many proposals for the exploitation of these data, but they all focus
on the resolution of specific problems, favouring the waste of information.
This work proposes a modular and flexible architecture to implement an intelligent
hybrid system for the analysis of multimedia content. Thanks to
the adaptation of the ETL concept and the application of data pipelines the
system can support the concurrence of several analysis processes running
in parallel. With the aim of verifying the potential of the proposed architecture,
two frameworks are designed. Both are oriented to the automatic
composition of descriptive music based on audiovisual content and they are
put into operation in two well-differentiated case studies where diverse metadata
extraction techniques and algorithms are applied within the context
of machine learning. The performance of the algorithms and the social acceptance
of the music are taken into account to validate the results obtained
in this work. In closing, the proposal favours the validation of the previously
established hypothesis, proving that the analysis of multimedia data through
AI techniques allows the creation of other relevant information. [EN]Great advances in the fields of Information and Communication Technologies,
IoT and AI have led to a series of systems whose use has increased
exponentially over the past few years. This has encouraged the generation
of huge amounts of data of a heterogeneous nature. The state of the art
gathers many proposals for the exploitation of these data, but they all focus
on the resolution of specific problems, favouring the waste of information.
This work proposes a modular and flexible architecture to implement an intelligent
hybrid system for the analysis of multimedia content. Thanks to
the adaptation of the ETL concept and the application of data pipelines the
system can support the concurrence of several analysis processes running
in parallel. With the aim of verifying the potential of the proposed architecture,
two frameworks are designed. Both are oriented to the automatic
composition of descriptive music based on audiovisual content and they are
put into operation in two well-differentiated case studies where diverse metadata
extraction techniques and algorithms are applied within the context
of machine learning. The performance of the algorithms and the social acceptance
of the music are taken into account to validate the results obtained
in this work. In closing, the proposal favours the validation of the previously
established hypothesis, proving that the analysis of multimedia data through
AI techniques allows the creation of other relevant information.
URI
DOI
10.14201/gredos.144227
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