An empirical evaluation of computational and perceptual multi-label genre classification on music / Christopher Sanden
Chris Sanden · Open ULeth Scholarship (OPUS) (University of Lethbridge) · 2010
Automatic music genre classification is a high-level task in the field of Music Information Retrieval (MIR).It refers to the process of automatically assigning genre labels to music for various tasks, including, but not limited to categorization, organization and browsing.This is a topic which has seen an increase in interest recently as one of the cornerstones of MIR.However, due to the subjective and ambiguous nature of music, traditional single-label classification is inadequate.In this thesis, we study multi-label music genre classification from perceptual and computational perspectives.First, we design a set of perceptual experiments to investigate the genre-labelling behavior of individuals.The results from these experiments lead us to speculate that multi-label classification is more appropriate for classifying music genres.Second, we design a set of computational experiments to evaluate multi-label classification algorithms on music.These experiments not only support our speculation but also reveal which algorithms are more suitable for music genre classification.Finally, we propose and examine a group of ensemble approaches for combining multi-label classification algorithms to further improve classification performance.I would like to express my deepest gratitude to my supervisor, Dr.