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Automatic classification of music genres based on constrained clustering

Grant number: 17/07525-2
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): August 01, 2017
Effective date (End): July 31, 2018
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Thiago Ferreira Covões
Grantee:Estevão Crippa da Veiga
Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil


Automatic classification of music genres is an important task for analyzing music data. One of the reasons that makes this task hard is the absence of precise definition about each genre. As a consequence, several taxonomies are adopted in different applications. In this work, we will assess the use of constrained clustering to identify clusters of musics. Such approach will make it possible to identify sub-genres without an a priori definition by the user. Evolutionary Algorithms will be used to perform the clustering such that an efficient search for Gaussian Mixture Models that best fit the data is performed. For assessing our approach, we will use benchmark datasets from the music information retrieval community. The candidate will learn and implement techniques related to Machine Learning and Evolutionary Algorithms. (AU)

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