Abstract
Musical pieces can be modeled as complex networks. This fosters innovative ways to categorize music, paving the way toward novel applications in multimedia domains, such as music didactics, multimedia entertainment, and digital music generation. Clustering these networks through their main metrics allows grouping similar musical tracks. To show the viability of the approach, we provide results on a dataset of guitar solos.
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