Classifying music into separate genres is an important task, which helps listeners discover new tracks, allows streaming services to better adjust to user preferences, and allows labels and music stores to advertise new albums more effectively. As the accessibility and ease-of-use of digital audio workstations increases, so does the quantity of music available to the public; additionally, differences between genres are not always well defined and can be abstract, with numerous records representing widely varying combinations of genres. In this article, multiple preprocessing methods and approaches to model training are described and compared, accounting for the eclectic nature of today's music. A custom and manually labeled dataset of more than 18000 entries has been used to perform the experiments.

Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data representation and analysis. However, no feature learning method has been proposed for such category of techniques. Here, we present an unsupervised feature learning mechanism that works on datasets with binary features. First, the dataset is mapped into a feature--sample network. Then, a multi-objective optimization process selects a set of new vertices to produce an enhanced version of the network. The new features depend on a nonlinear function of a combination of preexisting features. Effectively, the process projects the input data into a higher-dimensional space. To solve the optimization problem, we design two metaheuristics based on the lexicographic genetic algorithm and the improved strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced network contains more information and can be exploited to improve the performance of machine learning methods. The advantages and disadvantages of each optimization strategy are discussed.