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ISMIR
2004
Springer

Automatic Genre Classification Using Large High-Level Musical Feature Sets

11 years 6 months ago
Automatic Genre Classification Using Large High-Level Musical Feature Sets
This paper presents a system that extracts 109 musical features from symbolic recordings (MIDI, in this case) and uses them to classify the recordings by genre. The features used here are based on instrumentation, texture, rhythm, dynamics, pitch statistics, melody and chords. The classification is performed hierarchically using different sets of features at different levels of the hierarchy. Which features are used at each level, and their relative weightings, are determined using genetic algorithms. Classification is performed using a novel ensemble of feedforward neural networks and k-nearest neighbour classifiers. Arguments are presented emphasizing the importance of using high-level musical features, something that has been largely neglected in automatic classification systems to date in favour of low-level features. The effect on classification performance of varying the number of candidate features is examined in order to empirically demonstrate the importance of using a large ...
Cory McKay, Ichiro Fujinaga
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where ISMIR
Authors Cory McKay, Ichiro Fujinaga
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