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Algorithmic Songwriting with ALYSIA

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Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10198))

Abstract

This paper introduces ALYSIA: Automated LYrical SongwrIting Application. ALYSIA is based on a machine learning model using Random Forests, and we discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA was used to create original pop songs that were subsequently recorded and produced. Finally, we discuss our vision for the future of Automated Songwriting for both co-creative and autonomous systems.

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Notes

  1. 1.

    http://www.apple.com/logic-pro/.

  2. 2.

    https://www.ableton.com/en/live/.

  3. 3.

    http://www.apple.com/mac/garageband/.

  4. 4.

    Brown corpus: http://www.hit.uib.no/icame/brown/bcm.html.

  5. 5.

    CMUDict v0.07, Carnegie Mellon University.

  6. 6.

    Due to the difficulties in attaining MXL files, we are currently creating a new larger corpus of songs, which will be analyzed in future work.

References

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Author information

Authors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, California, USA

    Margareta Ackerman

  2. Orbitwerks, San Jose, California, USA

    David Loker

Authors
  1. Margareta Ackerman
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  2. David Loker
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Corresponding author

Correspondence to Margareta Ackerman .

Editor information

Editors and Affiliations

  1. University of Coimbra , Coimbra, Portugal

    João Correia

  2. RMIT University , Melbourne, Victoria, Australia

    Vic Ciesielski

  3. University of Malta , Msida, Malta

    Antonios Liapis

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Ackerman, M., Loker, D. (2017). Algorithmic Songwriting with ALYSIA. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-55750-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55749-6

  • Online ISBN: 978-3-319-55750-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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