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Abstract
The aim of this paper was to investigate the problem of music data processing and mining in large databases. Tests were performed on a large data-base that included approximately 30000 audio files divided into 11 classes cor-responding to music genres with different cardinalities. Every audio file was de-scribed by a 173-element feature vector. To reduce the dimensionality of data the Principal Component Analysis (PCA) with variable value of factors was em-ployed. The tests were conducted in the WEKA application with the use of k-Nearest Neighbors (kNN), Bayesian Network (Net) and Sequential Minimal Op-timization (SMO) algorithms. All results were analyzed in terms of the recogni-tion rate and computation time efficiency.
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Details
- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- ACTIVE MEDIA TECHNOLOGY, AMT 2014 strony 85 - 95
- Language:
- English
- Publication year:
- 2014
- Bibliographic description:
- Kostek B., Hoffmann P..: Music Data Processing and Mining in Large Databases for Active Media, W: ACTIVE MEDIA TECHNOLOGY, AMT 2014, 2014, Springer Verlag,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-09912-5_8
- 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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