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

TL;DR

This paper reviews the challenges and current state of music genre classification, highlighting issues like genre ambiguity, and presents an experiment comparing machine learning models on Audioset.

Contribution

It provides a comprehensive survey of music genre classification and introduces an experimental comparison of ML models using Audioset.

Findings

Different ML models show varying performance on genre classification

Genre ambiguity remains a significant challenge in MIR

New trends in machine learning are promising for music annotation

Abstract

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