Title page for etd-0909112-110138


[Back to Results | New Search]

URN etd-0909112-110138
Author Wei-Yu Chen
Author's Email Address No Public.
Statistics This thesis had been viewed 5562 times. Download 245 times.
Department Electrical Engineering
Year 2011
Semester 2
Degree Master
Type of Document
Language English
Title Forward-Selection-Based Feature Selection for Genre Analysis and Recognition of Popular Music
Date of Defense 2012-07-24
Page Count 62
Keyword
  • RBF (radial basis function)
  • SVM (support vector machine)
  • forward selection
  • Genre recognition
  • feature selection
  • Abstract In this thesis, a popular music genre recognition approach for Japanese popular music using SVM (support vector machine) with forward feature selection is proposed. First, various common acoustic features are extracted from the digital signal of popular music songs, including sub-bands, energy, rhythm, tempo, formants. A set of the most appropriate features for the genre identification is then selected by the proposed forward feature selection technique. Experiments conducted on the database consisting of 296 Japanese popular music songs demonstrate that the accuracy of recognition the proposed algorithm can achieve approximately 78.81% and the accuracy is stable when the number of testing music songs is increased.
    Advisory Committee
  • Ning-Han Liu - chair
  • Chun-Shien Lu - co-chair
  • Jau-Woei Perng - co-chair
  • Wan-Jen Huang - co-chair
  • Chia-Hung Yeh - advisor
  • Files
  • etd-0909112-110138.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2012-09-09

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have more questions or technical problems, please contact eThesys