The aim of this paper is to investigate music genre recognition in the rough set-based environment. Experiments involve a parameterized music data-base containing 1100 music excerpts. The database is divided into 11 classes cor-responding to music genres. Tests are conducted using the Rough Set Exploration System (RSES), a toolset for analyzing data with the use of methods based on the rough set theory. Classification effectiveness employing rough sets is compared against k-Nearest Neighbors (k-NN) and Local Transfer function classifiers (LTF-C). Results obtained are analyzed in terms of global class recognition and also per genre.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-319-19941-2_36
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2015
