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Brain Computer Interface, Visual Tracker and Artificial Intelligence for a Music Polyphony Generation System

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Human-Computer Interaction – INTERACT 2021 (INTERACT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12936))

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Abstract

In the Brain Computer Interface domain, studies on EEG represent a huge field of interest. Interactive systems that exploit low cost electroencephalographs to control machines are gaining momentum. Such technologies can be useful in the field of music and assisted composition. In this paper, a system that aims to generate four-part polyphonies is proposed. An artificial intelligence algorithm permits to generate polyphonies based on the N. Slonimsky’s theory by elaborating data coming from a Leap Motion device, to detect user’s hand movement, and a five-channel EEG signal detection device.

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Notes

  1. 1.

    In Fig. 1, the octave between the C note under the pentagram and the C note in the second upper space has been chosen and is represented in the Principal Tones section.

References

  1. Slonimsky, N.: 1984 – Thesaurus of Scales and Melodic Patterns. C. Scribner, New York (1947). Schirmer Books, s division of Macmillan Publishing Co., Inc., New York

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  2. Ultraleap. Leap Motion Controller. https://www.ultraleap.com/product/leap-motion-controller/. Accessed April 2021

  3. Emotiv Insight 5-channel mobile EEG. https://www.emotiv.com/product/emotiv-insight-5-channel-mobile-eeg/. Accessed April 2021

  4. Emotiv Cortex API. https://emotiv.gitbook.io/cortex-api/. Accessed April 2021

  5. Keras Framework. https://keras.io/. Accessed April 2021

  6. Max/Msp. https://cycling74.com/. Accessed April 2021

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Acknowledgments

The authors acknowledge partial support of the following projects: PON Casa delle tecnologie emergenti di Matera “CTEMT” (CUP I14E20000020001), Servizi Locali 2.0, PON ARS01_00876 Bio-D, PON ARS01_00821 FLET4.0, PON ARS01_00917 OK-INSAID, H2020 PASSPARTOUT.

Author information

Authors and Affiliations

  1. Politecnico di Bari, Via E. Orabona 4, 70125, Bari, Italy

    Carmelo Ardito, Tommaso Colafiglio, Tommaso Di Noia & Eugenio Di Sciascio

Authors
  1. Carmelo Ardito
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  2. Tommaso Colafiglio
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  3. Tommaso Di Noia
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  4. Eugenio Di Sciascio
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Corresponding author

Correspondence to Carmelo Ardito .

Editor information

Editors and Affiliations

  1. Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy

    Carmelo Ardito

  2. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Rosa Lanzilotti

  3. Computer Science Department, University of Pisa, Pisa, Italy

    Alessio Malizia

  4. Department of Computer Science, University of York, York, UK

    Helen Petrie

  5. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Antonio Piccinno

  6. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Giuseppe Desolda

  7. Microsoft Research, Redmond, WA, USA

    Kori Inkpen

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Cite this paper

Ardito, C., Colafiglio, T., Di Noia, T., Di Sciascio, E. (2021). Brain Computer Interface, Visual Tracker and Artificial Intelligence for a Music Polyphony Generation System. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12936. Springer, Cham. https://doi.org/10.1007/978-3-030-85607-6_39

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  • DOI: https://doi.org/10.1007/978-3-030-85607-6_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85606-9

  • Online ISBN: 978-3-030-85607-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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