Statistical Analysis of features used in Automatic Audio Genre Classification
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Abstract
This paper presents statistical models for some of the most important features used to classify audio signals into musical genres. The genres used here are selected according to a given taxonomy. The features are computed for each genre using the signals from a dataset and the results are grouped into histograms. Each proposed statistical model consists of an estimated Probability Density Function (PDF), optimized to best fit a determined histogram and the optimization criterion is the minimization of the Mean Square Error (MSE). Finally, the paper discusses how these models can be applied to classify audio signals into genres.
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How to Cite
G. A. Barbedo, J., & Lopes, A. (2015). Statistical Analysis of features used in Automatic Audio Genre Classification. Journal of Communication and Information Systems, 21(2). https://doi.org/10.14209/jcis.2006.9
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Regular Papers
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