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Music Genre Classification: Looking for the Perfect Network

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12742))

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Abstract

This paper presents research on music genre recognition. It is a crucial task because there are millions of songs in the online databases. Classifying them by a human being is impossible or extremely expensive. As a result, it is desirable to create methods that can assign a given track to a music genre. Here, the classification of music tracks is carried out by deep learning models. The Free Music Archive dataset was used to perform experiments. The tests were executed with the usage of Convolutional Neural Network, Convolutional Recurrent Neural Networks with 1D and 2D convolutions, and Recurrent Neural Network with Long Short-Term Memory cells. In order to combine the advantages of different deep neural network architectures, a few types of ensembles were proposed with two types of results mixing methods. The best results obtained in this paper, which are equal to state-of-the-art methods, were achieved by one of the proposed ensembles. The solution described in the paper can help to make the auto-tagging of songs much faster and more accurate in the context of assigning them to particular musical genres.

This work was supported by Statutory Research funds of Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland (BKM21 – DK, BK 02/100/BK_21/0008 – RB).

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Author information

Authors and Affiliations

  1. Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa, Piotr Kaminski & Robert Brzeski

Authors
  1. Daniel Kostrzewa
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  2. Piotr Kaminski
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  3. Robert Brzeski
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Corresponding author

Correspondence to Daniel Kostrzewa .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Kostrzewa, D., Kaminski, P., Brzeski, R. (2021). Music Genre Classification: Looking for the Perfect Network. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-77961-0_6

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