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.

中文翻译：

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电子舞曲分类中的自动子流派分类

电子舞曲 (EDM) 是一种每周都会发布数千首新歌曲的流派。考虑的 EDM 子流派列表很长，但它也会根据趋势和音乐品味而发展。有鉴于此，我们检索了相隔一年多的两套2000多首歌曲。歌曲属于 EDM 网站分类法的前 100 名列表，该分类法包含 20 多个在考虑的时期内发生变化的子流派。我们在这些集合上测试自动分类的有效性并深入研究结果以确定，例如，哪些子流派表现更好和更差，两个集合中某些子流派的表现如何变化，或者某些子流派如何经常相互混淆. 我们通过图表说明子类型之间的混淆，并将其解释为 EDM 的分类图。我们还评估了第一组分类器在用于对第二组分类器进行分类时性能的恶化。最后，我们在第一组分类器的帮助下研究出现在第二组中的新子类型与旧子类型之间的关系。因此，这项工作说明了 EDM 给自动分类带来的主要挑战，并提供了有关这种方法的局限性在哪里的见解。