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Music Genre Classification Using a Time-Delay Neural Network

  • Jae-Won Lee
  • Soo-Beom Park
  • Sang-Kyoon Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

A method is proposed for classifying music genre for audio retrieval systems using time-delay neural networks. The proposed classification method considers eight types of music genre: Blues, Country, Hard Core, Hard Rock, Jazz, R&B(Soul), Techno, and Trash Metal. The melody between bars in the music is used to distinguish the different genres. The melody pattern is extracted based on the sound of a snare drum, which is used to effectively represent the rhythm periodicity. Classification is based on a time-delay neural network that uses a Fourier transformed vector of the melody as an input pattern. This classification method was used to analyze 80 training data from ten different musical pieces for each genre and a further 40 test data from five additional musical pieces for each genre. The accuracy of the genre classifications that were obtained for the two sets of data was 92.5% and 60%, respectively.

Keywords

Hide Layer Training Pattern Audio Data Musical Piece Spatial Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jae-Won Lee
    • 1
  • Soo-Beom Park
    • 1
  • Sang-Kyoon Kim
    • 1
  1. 1.Department of Computer ScienceInje UniversityKimhaeKorea

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