Distribution-based Classification of Musical Genre Using Fractal Dimension
نویسنده
چکیده
Automatic genre classification of audio signals is an open challenge of pattern recognition. The fractal nature of the music signals indicates the Hausdorff dimension as a distinguish feature for classifying audio tracks. However this key parameter cannot be unequivocally defined since it is not constant during the whole track, but it ranges in a wide set of values. The aim of this paper is to face the classification task by comparing the empirical distributions of the fractal dimensions recorded during each track. We show that this single feature allows to achieve very promising results, especially in view of designing more complex classifiers that combine it with other commonly used descriptors. Key-Words: Genre classification, fractal dimension, KL divergence.
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