Abstract
This document presents an analysis of Music Knowledge as a first step towards music representation for composition. After an introductory review of music computing evolution, several approaches to music knowledge are described: the system levels context, music theory and disciplines, dimensions in music, and finally the creative process. Then, the composition knowledge is analyzed at the symbolic level, dissecting its sub-level structure, and concluding with some requirements for an efficient representation. EV meta-model is presented as a multilevel representation tool for event based systems as music. Its structure and unique features are described within the analyzed level context. Three musical application examples of EV modeling are shown in the field of sound synthesis and music composition. These examples test representation, extension and development features.
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© 2006 Springer-Verlag Berlin Heidelberg
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Alvaro, J.L., Miranda, E.R., Barros, B. (2006). Music Knowledge Analysis: Towards an Efficient Representation for Composition. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_35
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DOI: https://doi.org/10.1007/11881216_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45914-9
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