I forget where I got this reference, but the article is about the use of automated techniques to classify recorded music into genres according to rhythmic patterns. It is well, if perhaps only informally, known that we can recognize familiar tunes even if we only here the melody's rhythm.
New J. Phys. 12 (2010) 053030
doi:10.1088/1367-2630/12/5/053030
Debora C Correa1, Jose H Saito and Luciano da F Costa
E-mail: deboracorrea@ursa.ifsc.usp.br and luciano.if.sc.usp.br
Published 20 May 2010
Abstract. Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multivariate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.
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