Subgenre classification of rock music using support
Name
Elinor Toodo
Abstract
This paper focuses on performing automatic genre classification using subgenres of rock music. The purpose of this paper is to see how well it can be done and whether subgenre classification has potential for the future. Suport vector machines were chosen for this task. Overviews of the extracted features, used genre groups, and the basic ideas behind support vector machines are presented. For the purpose of this work, a dataset of five different subgenre groups was constructed. The groups were as follows: progressive rock, punk rock, general metal, extreme metal, and general rock music. A total of 500 songs was used, of which 400 songs was used to train the model and 100 songs was use to test it. Features were extracted using jAudio and classification task was done with Weka. Highest result achieved was the classification acuracy of 71%. With the use of interquartile ranges the accuracy reached 74%.
Graduation Thesis language
Estonian
Graduation Thesis type
Bachelor - Information Technology
Supervisor(s)
Sven Aller, Margus Niitsoo
Defence year
2013