Department of Software and Computing Systems


Title:Similarity Learning and Stochastic Language Models for Tree-Represented Music Import to your calendar:
Presenter:Jose Francisco Bernabeu Briones
Venue:Sala Claude Shannon
Date&time:20/07/2017 11:00
Estimated duration:2:00 horas
Contact person: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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