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Music Genre Classification Using a Gradient-Based Local Texture Descriptor

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Intelligent Decision Technologies 2016

Part of the book series: Smart Innovation, Systems and Technologies ((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.

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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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Authors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Faisal Ahmed, Padma Polash Paul & Marina Gavrilova

Authors
  1. Faisal Ahmed
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  2. Padma Polash Paul
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  3. Marina Gavrilova
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Corresponding author

Correspondence to Faisal Ahmed .

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Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, UK

    Robert J. Howlett

  4. University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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© 2016 Springer International Publishing Switzerland

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Ahmed, F., Paul, P.P., Gavrilova, M. (2016). Music Genre Classification Using a Gradient-Based Local Texture Descriptor. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies 2016. Smart Innovation, Systems and Technologies, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-39627-9_40

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  • DOI: https://doi.org/10.1007/978-3-319-39627-9_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39626-2

  • Online ISBN: 978-3-319-39627-9

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