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Abstract
The paper focuses on optimization vector content feature for the music recommendation system. For the purpose of experiments a database is created consisting of excerpts of music les. They are assigned to 22 classes corresponding to dierent music genres. Various feature vectors based on low-level signal descriptors are tested and then optimized using correlation analysis and Principal Component Analysis (PCA). Results of the experiments are shown for the variety of feature vectors. Also, a music recommendatio
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- Category:
- Articles
- Type:
- artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
- Published in:
-
Journal of Telecommunications and Information Technology
pages 59 - 69,
ISSN: 1509-4553 - Language:
- English
- Publication year:
- 2014
- Bibliographic description:
- Hoffmann P., Kaczmarek A., Spaleniak P., Kostek B.: Music Recommendation System// Journal of Telecommunications and Information Technology. -., nr. 2 (2014), s.59-69
- 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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