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International Conference on Knowledge Science, Engineering and Management

KSEM 2019: Knowledge Science, Engineering and Management pp 365–372Cite as

Music Genre Classification via Sequential Wavelet Scattering Feature Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11776))

Abstract

Various content-based high-level descriptors are used for musical similarity, classification and recommendation tasks. Our study uses wavelet scattering coefficients as features providing both translation-invariant representation and transient characterizations of audio signal to predict musical genre. Extracted features are fed to sequential architectures to model temporal dependencies of musical piece more efficiently. Competitive classification results are obtained against hand-engineered feature based frameworks with proposed technique.

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Notes

  1. 1.

    We used various combinations of wavelet scattering parameters of N: transform support length of input signal length, \(2^J\): maximum log-scale of the scattering transform and Q: The number of first-order wavelets per octave for; \(N \in [2^{15},2^{16},2^{17}]\), \(J \in [2^{10}, 2^{11}, 2^{12}]\) and \(Q \in [8,10,12]\).

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Yildiz Technical University (YTU), 34220, Istanbul, Turkey

    Evren Kanalici & Gokhan Bilgin

  2. Signal and Image Processing Lab. (SIMPLAB) at YTU, 34220, Istanbul, Turkey

    Evren Kanalici & Gokhan Bilgin

Authors
  1. Evren Kanalici
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  2. Gokhan Bilgin
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Corresponding author

Correspondence to Gokhan Bilgin .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Kanalici, E., Bilgin, G. (2019). Music Genre Classification via Sequential Wavelet Scattering Feature Learning. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_32

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  • DOI: https://doi.org/10.1007/978-3-030-29563-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29562-2

  • Online ISBN: 978-3-030-29563-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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