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Music Genre Classification Using a Gradient-Based Local Texture Descriptor

  • Faisal Ahmed
  • Padma Polash Paul
  • Marina Gavrilova
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)

Abstract

With the increasing popularity and availability of online music databases that store vast collections of music, automated classification of music genre has attracted significant attention for the management of such large-scale databases. This paper presents a new music genre classification method that utilizes gradient-based texture analysis of the spectrograms constructed from the audio signals. We propose to use gradient directional pattern (GDP)—a robust local texture descriptor that exploits the gradient directional information to encode the local texture properties of an image. The proposed method first computes spectrograms from the audio signals and then applies the GDP operator to construct the feature descriptors that represent micro-level texture details of the spectrograms. We use a support vector machine (SVM) for the classification task. The effectiveness of the proposed method is evaluated using the GTZAN genre collection music database. Our experiments show promising results for the proposed GDP-based spectrogram texture analysis, as compared against some other existing music genre classification methods.

Keywords

Music genre classification Local texture analysis Spectrogram Gradient directional pattern (GDP) 

Notes

Acknowledgments

The authors would like to thank NSERC Discovery Grant Project 1028463, NSERC Engage, AITF, and MITACS Accelerate for partial support of this project.

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© Springer International Publishing Switzerland 2016

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Authors and Affiliations

  • Faisal Ahmed
    • 1
  • Padma Polash Paul
    • 1
  • Marina Gavrilova
    • 1
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada

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