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International Conference on Active Media Technology

AMT 2014: Active Media Technology pp 85–95Cite as

Music Data Processing and Mining in Large Databases for Active Media

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8610))

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 database that included approximately 30000 audio files divided into 11 classes corresponding to music genres with different cardinalities. Every audio file was described by a 173-element feature vector. To reduce the dimensionality of data the Principal Component Analysis (PCA) with variable value of factors was employed. The tests were conducted in the WEKA application with the use of k-Nearest Neighbors (kNN), Bayesian Network (Net) and Sequential Minimal Optimization (SMO) algorithms. All results were analyzed in terms of the recognition rate and computation time efficiency.

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Author information

Authors and Affiliations

  1. Faculty o Electronics, Telecommunications and Informatics, Audio Acoustics Laboratory, Gdańsk University of Technology, Gdańsk, 80-233, Poland

    Piotr Hoffmann & Bożena Kostek

Authors
  1. Piotr Hoffmann
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  2. Bożena Kostek
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślȩzak

  2. Department of Computer Science, Loughborough University, Loughborough, U.K.

    Gerald Schaefer

  3. Computer Science Department, University of British Columbia, 2366 Main Mall, P.O. Box, Vancouver, B.C., Canada

    Son T. Vuong

  4. Department of Information & Communication Engineering, Inha University, Korea

    Yoo-Sung Kim

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© 2014 Springer International Publishing Switzerland

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Hoffmann, P., Kostek, B. (2014). Music Data Processing and Mining in Large Databases for Active Media. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, YS. (eds) Active Media Technology. AMT 2014. Lecture Notes in Computer Science, vol 8610. Springer, Cham. https://doi.org/10.1007/978-3-319-09912-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-09912-5_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09911-8

  • Online ISBN: 978-3-319-09912-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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