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A Software Tool for Categorizing Violin Student Renditions by Comparison

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Advances in Artificial Intelligence (CAEPIA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11160))

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Abstract

Music Education is a challenging domain for Artificial Intelligence because of the inherent complexity of music, an activity with no clear goals and no comprehensive set of well-defined methods for success. It involves complementary aspects from other disciplines such as psychology and acoustics; and it also requires creativity and eventually, some manual abilities. In this paper, we present an application of machine learning to the learning of music performance. Our devised system is able to discover the similarities and differences between a given performance and those from other musicians. Such a system would be of great value to music students when learning how to perform a certain piece of music.

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Acknowledgements

M. Molina-Solana is funded by the EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 743623.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada, Spain

    Miguel Delgado, Waldo Fajardo & Miguel Molina-Solana

Authors
  1. Miguel Delgado
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  2. Waldo Fajardo
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  3. Miguel Molina-Solana
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Corresponding author

Correspondence to Miguel Molina-Solana .

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Editors and Affiliations

  1. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Francisco Herrera

  2. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Damas

  3. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Rosana Montes

  4. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Alonso

  5. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Óscar Cordón

  6. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Antonio González

  7. School of Engineering, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso

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Delgado, M., Fajardo, W., Molina-Solana, M. (2018). A Software Tool for Categorizing Violin Student Renditions by Comparison. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_31

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  • DOI: https://doi.org/10.1007/978-3-030-00374-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00373-9

  • Online ISBN: 978-3-030-00374-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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