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Abstract
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.
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- Pattern Recognition and Machine Intelligence strony 377 - 386
- Language:
- English
- Publication year:
- 2015
- Bibliographic description:
- Hoffmann P., Kostek B.: Music Genre Recognition in the Rough Set-Based Environment// Pattern Recognition and Machine Intelligence/ ed. Marzena Kryszkiewicz : Springer, 2015, s.377-386
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-19941-2_36
- Verified by:
- Gdańsk University of Technology
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