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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2019: Artificial Intelligence and Soft Computing pp 143–152Cite as

Application of Deep Neural Networks to Music Composition Based on MIDI Datasets and Graphical Representation

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11508)

Abstract

In this paper we have presented a method for composing and generating short musical phrases using a deep convolutional generative adversarial network (DCGAN). We have used a dataset of classical and jazz music MIDI recordings in order to train the network. Our approach introduces translating the MIDI data into graphical images in a piano roll format suitable for the DCGAN, using the RGB channels as additional information carriers for improved performance. We show that the network has learned to generate images that are indistinguishable from the input data and, when translated back to MIDI and played back, include several musically interesting rhythmic and harmonic structures. The results of the conducted experiments are described and discussed, with conclusions for further work and a short comparison with selected existing solutions.

Keywords

  • AI
  • Artificial intelligence
  • Neural networks
  • GAN
  • Music
  • MIDI

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    While the sustain pedal is pressed, the notes of the piano sustain after keys are released by the pianist.

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Author information

Authors and Affiliations

  1. Division of Computer Graphics, Institute of Computer Science, The Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Mateusz Modrzejewski, Mateusz Dorobek & Przemysław Rokita

Authors
  1. Mateusz Modrzejewski
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  2. Mateusz Dorobek
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  3. Przemysław Rokita
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Corresponding author

Correspondence to Mateusz Modrzejewski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Prof. Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Dr. Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Dr. Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Prof. Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Dr. Jacek M. Zurada

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Modrzejewski, M., Dorobek, M., Rokita, P. (2019). Application of Deep Neural Networks to Music Composition Based on MIDI Datasets and Graphical Representation. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_14

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  • DOI: https://doi.org/10.1007/978-3-030-20912-4_14

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  • Print ISBN: 978-3-030-20911-7

  • Online ISBN: 978-3-030-20912-4

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