Feature Extraction Using Pitch Class Profile Information Entropy
Identifieur interne : 000575 ( Main/Exploration ); précédent : 000574; suivant : 000576Feature Extraction Using Pitch Class Profile Information Entropy
Auteurs : A. Kaliakatsos-Papakostas [Grèce] ; G. Epitropakis [Grèce] ; N. Vrahatis [Grèce]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
English descriptors
Abstract
Abstract: Computer aided musical analysis has led a research stream to explore the description of an entire musical piece by a single value. Combinations of such values, often called global features, have been used for several identification tasks on pieces with symbolic music representation. In this work we extend some ideas that estimate information entropy of sections of musical pieces, to utilize the Pitch Class Profile information entropy for global feature extraction. Two approaches are proposed and tested, the first approach considers musical sections as overlapping sliding onset windows, while the second one as non-overlapping fixed-length time windows.
Url:
DOI: 10.1007/978-3-642-21590-2_32
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Abstract: Computer aided musical analysis has led a research stream to explore the description of an entire musical piece by a single value. Combinations of such values, often called global features, have been used for several identification tasks on pieces with symbolic music representation. In this work we extend some ideas that estimate information entropy of sections of musical pieces, to utilize the Pitch Class Profile information entropy for global feature extraction. Two approaches are proposed and tested, the first approach considers musical sections as overlapping sliding onset windows, while the second one as non-overlapping fixed-length time windows.</div>
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