Departamento de Lenguajes y Sistemas Informáticos


Título:Similarity Learning and Stochastic Language Models for Tree-Represented Music Incorpóralo a tu calendario:
Tipo:lectura de tesis doctoral
Por:Jose Francisco Bernabeu Briones
Lugar:Sala Claude Shannon
Día/hora:11:00 20/07/2017
Duración aproximada:2:00 horas
Persona de contacto:

Micó Andrés, María Luisa (mico[Perdone'm]
Similarity computation is a difficult issue in music
information retrieval tasks, because it tries to emulate the special ability
that humans show for pattern recognition in general, and particularly in
the presence of noisy data. A number of works have addressed the problem
of what is the best representation for symbolic music in this context. The
tree representation, using rhythm for defining the tree structure and pitch
information for leaf and node labelling has proven to be effective in melodic
similarity computation. In this dissertation we try to built a system that
allowed to classify and generate melodies using the information from the tree
encoding, capturing the inherent dependencies which are inside this kind of
structure, and improving the current methods in terms of accuracy and running
time. In this way, we try to find more efficient methods that is key to use
the tree structure in large datasets. First, we study the possibilities of the
tree edit similarity to classify melodies using a new approach for estimate
the weights of the edit operations. Once the possibilities of the cited
approach are studied, an alternative approach is used. For that a grammatical
inference approach is used to infer tree languages. The inference of these
languages give us the possibility to use them to classify new trees (melodies).
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