Automatic classification of drum sounds a comparison of feature selection methods and classification techniques

TitleAutomatic classification of drum sounds a comparison of feature selection methods and classification techniques
Publication TypeConference Paper
Year of Publication2002
Conference NameInternational Conference on Music and Artificial Intelligence (ICMAI)
AuthorsHerrera, P., Yeterian A., & Gouyon F.
Conference Start Date12/09/2002
Conference LocationEdinburgh, Scotland
AbstractWe 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 hitrates have been achieved (99%, 97%, and 90% respectively) with several of the tested techniques.

 

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