In this paper we present the music information plane and the
dfferent levels of information extraction that exist in the musical domain.
Based on this approach we propose a way to overcome the existing semantic
gap in the music field. Our approximation is twofold: we propose
a set of music descriptors that can automatically be extracted from the
audio signals, and a top-down approach that adds explicit and formal
semantics to these annotations. These music descriptors are generated
in two ...
In this paper we present the music information plane and the
dfferent levels of information extraction that exist in the musical domain.
Based on this approach we propose a way to overcome the existing semantic
gap in the music field. Our approximation is twofold: we propose
a set of music descriptors that can automatically be extracted from the
audio signals, and a top-down approach that adds explicit and formal
semantics to these annotations. These music descriptors are generated
in two ways: as derivations and combinations of lower-level descriptors
and as generalizations induced from manually annotated databases by
the intensive application of machine learning. We belive that merging
both approaches (bottom-up and top-down) can overcome the existing
semantic gap in the musical domain.
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