Classification of Heavy Metal Subgenres with Machine Learning
Rönnberg, Thomas (2020)
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The music industry is undergoing an extensive transformation as a result of growth in streaming data and various AI technologies, which allow for more sophisticated marketing and sales methods. Since consumption patterns vary by different factors such as genre and engagement, each customer segment needs to be targeted uniquely for maximal efficiency. A challenge in this genre-based segmentation method lies in today’s large music databases and their maintenance, which have shown to require exhausting and time-consuming work. This has led to automatic music genre classification (AMGC) becoming the most common area of research within the growing field of music information retrieval (MIR). A large portion of previous research has been shown to suffer from serious integrity issues. The purpose of this study is to re-evaluate the current state of applying machine learning for the task of AMGC. Low-level features are derived from audio signals to form a custom-made data set consisting of five subgenres of heavy metal music. Different parameter sets and learning algorithms are weighted against each other to derive insights into the success factors. The results suggest that admirable results can be achieved even with fuzzy subgenres, but that a larger number of high-quality features are needed to further extend the accuracy for reliable real-life applications.