Automatic Classification of Drum Sounds: A Comparison of Feature Selection Methods and Classification Techniques View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002

AUTHORS

Perfecto Herrera , Alexandre Yeterian , Fabien Gouyon

ABSTRACT

We present a comparative evaluation of automatic classification of a sound database containing more than six hundred drum sounds (kick, snare, hihat, toms and cymbals). A preliminary set of fifty descriptors has been refined with the help of different techniques and some final reduced sets including around twenty features have been selected as the most relevant. We have then tested different classification techniques (instance-based, statistical-based, and tree-based) using ten-fold cross-validation. Three levels of taxonomic classification have been tested: membranes versus plates (super-category level), kick vs. snare vs. hihat vs. toms vs. cymbals (basic level), and some basic classes (kick and snare) plus some sub-classes -i.e. ride, crash, open-hihat, closed hihat, high-tom, medium-tom, low-tom- (sub-category level). Very high hit-rates have been achieved (99%, 97%, and 90% respectively) with several of the tested techniques. More... »

PAGES

69-80

References to SciGraph publications

Book

TITLE

Music and Artificial Intelligence

ISBN

978-3-540-44145-8
978-3-540-45722-0

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-45722-4_8

DOI

http://dx.doi.org/10.1007/3-540-45722-4_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047162282


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