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      Conditional GAN for Diatonic Harmonic Sequences Generation in a VR Context

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      proceedings-article
      ,
      Proceedings of EVA London 2021 (EVA 2021)
      AI and the Arts: Artificial Imagination
      5th July – 9th July 2021
      Conditional GAN, Harmonic sequences generation, VR, Structural harmony method, Computer-aided composition
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            Abstract

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            Author and article information

            Contributors
            Conference
            July 2021
            July 2021
            : 97-100
            Affiliations
            [0001]FabLab by Inetum

            157 Boulevard McDonald, 75019 Paris, France
            [0002]iMSA

            Rue Clos Maury 82000 Montauban, France
            Article
            10.14236/ewic/EVA2021.15
            d61e6e9b-529c-4248-a8e9-27a5f188db36
            © Shvets et al. Published by BCS Learning & Development Ltd. Proceedings of EVA London 2021, UK

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Proceedings of EVA London 2021
            EVA 2021
            London
            5th July – 9th July 2021
            Electronic Workshops in Computing (eWiC)
            AI and the Arts: Artificial Imagination
            History
            Product

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2021.15
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            Structural harmony method,Conditional GAN,VR,Harmonic sequences generation,Computer-aided composition

            REFERENCES

            1. and (2010) "music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data," International Conference on Music Information Retrieval, pp. 637–642.

            2. , , and , 2018, April. Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).

            3. and (2002). Finding Temporal Structure in Music: Blues Improvisation with LSTM Recurrent Networks 12th IEEE Workshop on Neural Networks for Signal Processing.

            4. , , , , , , , , and , 2018. Music transformer. arXiv preprint arXiv:1809.04281.

            5. , , , and (2020). On the Adaptability of Recurrent Neural Networks for Real-Time Jazz Improvisation Accompaniment. Frontiers in Artificial Intelligence, 3, p.113.

            6. (2016). Bachbot: Automatic Composition in the s Style of Bach Chorales. M.Phil thesis. University of Cambridge.

            7. (2019). Structural harmony method in the context of deep learning on example of music by Valentyn Sylvestrov and Philipp Glass. In: , , , and (eds), EVA London 2019 (Electronic Visualisation and the Arts), London, UK, 10–14 July 2019, 318–320. BCS, London. [Cross Ref]

            8. , (2020). Graphs in harmony learning: AI assisted VR application. In: , , , and (eds), EVA London 2020 (Electronic Visualisation and the Arts), London, UK, 6–9 July 2020, 104–105. BCS, London. [Cross Ref]

            9. , and (2015). Modelling Temporal Dependencies in Data Using a DBNLSTM. IEEE International Conference on Data Science and Advanced Analytics.

            10. , , & (2017). MidiNet: A convolutional generative adversarial network for symbolic-domain music generation. arXiv preprint arXiv:1703.10847.

            11. , , and (2017). Seqgan: Sequence generative adversarial nets with policy gradient. AAAI conference on artificial intelligence (Vol. 31, No. 1).

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