Automatic subgenre classification in an electronic dance music taxonomy

Abstract

Electronic dance music (EDM) is a genre where thousands of new songs are released every week. The list of EDM subgenres considered is long, but it also evolves according to trends and musical tastes. With this in view, we have retrieved two sets of over 2000 songs separated by more than a year. Songs belong to the top 100 list of an EDM website taxonomy of more than 20 subgenres that changed in the period considered. We test the effectiveness of automatic classification on these sets and delve into the results to determine, for example, which subgenres perform better and worse, how the performance of some subgenres change in the two sets, or how some subgenres are often confused with one another. We illustrate confusion among subgenres by a graph and interpret it as a taxonomic map of EDM. We also assess the deterioration of the performance of the classifier of the first set when used to classify the second one. Finally, we study how the new subgenres that appear in the second set relate to the old ones with the help of the classifier of the first set. As a result, this work illustrates the main challenges that EDM poses to automatic classification and provides insights into where are the limits of this approach.