Electronic Music is very diverse and covers many genres, each one of them with very distinct characteristics. It is easy to distinguish between some of them, and, for some others, its is not so easy. When it comes to subgenres of a main genre this becomes much more difficult since the differences become very subtle. For two different people the same track may be classified as belonging to completely different subgenres. Given the relevance of the subject, the hereby proposed project takes on the duty to implement and evaluate a model to classify electronic music according to its genre using Machine Learning and Digital Signal Processing techniques and concepts. The main objective here is to build a music genre classification model capable of achieving satisfactory results using a small amount of training data.