The Clever Toolbox - the Art of Automated Genre Classification

Peter Ahrendt, Anders Meng, Jan Larsen, Sune Lehmann

AbstractAutomatic musical genre classification can be defined as the science of finding computer algorithms that a digitized sound clip as input and yield a musical genre as output. The goal of automated genre classification is, of course, that the musical genre should agree with the human classificasion.

This demo illustrates an approach to the problem that first extract frequency-based sound features followed by a "linear regression" classifier. The basic features are the so-called mel-frequency cepstral coefficients (MFCCs), which are extracted on a time-scale of 30 msec. From these MFCC features, auto-regressive coefficients (ARs) are extracted along with the mean and gain to get a single (30 dimensional) feature vector on the time-scale of 1 second. These features have been used because they have performed well in a previous study (Meng, Ahrendt, Larsen (2005)). Linear regression (or single-layer linear NN) is subsequently used for classification. This classifier is rather simple; current research investigates more advanced methods of classification.
Keywordsmusic genre classification MFCC features
PublisherISP Group, Informatics and Mathematical Modelling, Tehcnical University of Denmark
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, Denmark
Electronic version(s)[zip]
Demo link
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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