Since the early days of the information era, digital music has been becoming one of the most consumed types of media, introducing\nthe need for content-based tools that can search, browse, and retrieve music. Here we describe a method that can\nquantify similarities between musical genres in an unsupervised fashion, and computes networks of similarities between different\nmusicians or musical styles. The method works by converting each song to its 2D spectrogram, and then extracting a large\nset of 2883 2D numerical content descriptors. The descriptors are weighted by their informativeness, and then the similarities\nbetween the musical styles are measured using the weighted distances between the musical pieces of each pair of musicians or\ngenres. The similarities between all pairs provide a similarity matrix, which is visualized by a phylogeny. Experiments using\n23 well known musicians representing seven musical genres show that the algorithm was able to separate the artists into groups\nthat are in agreement with their respective musical genres. The analysis was done in an unsupervised fashion, and without any\nhuman definition or annotation of the musical styles.
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