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Feature-Driven Classification of Musical Styles
Abstract:In this paper we address the problem of musical style classification, which has a number of applications like indexing in musical databases or automatic composition systems. Starting from MIDI files of real-world improvisations, we extract the melody track and cut it into overlapping segments of equal length. From these fragments, some numerical features are extracted as descriptors of style samples. We show that a standard Bayesian classifier can be conveniently employed to build an effective musical style classifier, once this set of features has been extracted from musical data. Preliminary experimental results show the effectiveness of the developed classifier that represents the first component of a musical audio retrieval system
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061453Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 929
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