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Music genre classification is one of the sub-disciplines of music informationretrieval (MIR) with growing popularity among researchers, mainly due to thealready open challenges. Although research has been prolific in terms of numberof published works, the topic still suffers from a problem in its foundations:there is no clear and formal definition of what genre is. Music categorizationsare vague and unclear, suffering from human subjectivity and lack of agreement.In its first part, this paper offers a survey trying to cover the manydifferent aspects of the matter. Its main goal is give the reader an overviewof the history and the current state-of-the-art, exploring techniques anddatasets used to the date, as well as identifying current challenges, such asthis ambiguity of genre definitions or the introduction of human-centricapproaches. The paper pays special attention to new trends in machine learningapplied to the music annotation problem. Finally, we also include a music genreclassification experiment that compares different machine learning models usingAudioset.

Machine learning for music genre: multifaceted review and experimentation with audioset