Automatic music genre classification based on musical instrument track separation / Automatyczna klasyfikacja gatunku muzycznego wykorzystująca algorytm separacji dźwięku instrumentó muzycznych
Abstract
The aim of this article is to investigate whether separating music tracks at the pre-processing phase and extending feature vector by parameters related to the specific musical instruments that are characteristic for the given musical genre allow for efficient automatic musical genre classification in case of database containing thousands of music excerpts and a dozen of genres. Results of extensive experiments show that the approach proposed for music genre classification is promising. Overall, conglomerating parameters derived from both an original audio and a mixture of separated tracks improve classification effectiveness measures, demonstrating that the proposed feature vector and the Support Vector Machine (SVM) with Co-training mechanism are applicable to a large dataset.
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/s10844-017-0464-5
- License
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Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
no. 50,
pages 363 - 384,
ISSN: 0925-9902 - Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Rosner A., Kostek B.: Automatic music genre classification based on musical instrument track separation / Automatyczna klasyfikacja gatunku muzycznego wykorzystująca algorytm separacji dźwięku instrumentó muzycznych// JOURNAL OF INTELLIGENT INFORMATION SYSTEMS. -Vol. 50, nr. 2 (2018), s.363-384
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/s10844-017-0464-5
- Verified by:
- Gdańsk University of Technology
Referenced datasets
- dataset SYNAT_MUSIC_GENRE_FV_173
- dataset SYNAT Music Genre Parameters PCA 19
- dataset SYNAT_PCA_48
- dataset SYNAT_PCA_11
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