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      Structural Harmony Method in the Context of Deep Learning on Example of Music by Valentyn Sylvestrov and Philipp Glass

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      proceedings-article
       
      Proceedings of EVA London 2019 (EVA 2019)
      Electronic Visualisation and the Arts
      8 - 11 July 2019
      Harmonic sequences clustering, Supervised learning, Deep learning, CNN, Convolutional LSTM
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            Abstract

            The article shows the potential of structural harmony method application in deep learning models training. Harmonic sequences, being represented as the paths in the system of graphs and then schematised, serve as the source of the deep learning models input. The visual character of the dataset generated using schemes allows the application of supervised machine learning technics and is suitable for time series analysis. Therefore, two neural network architectures – Convolutional and Convolutional Long-Short Term Memory were tested on example of music by postmodern composers Valentyn Sylvestrov (represented by vocal cycle “Silent songs”) and Philipp Glass (represented by piano cycle “Metamorphosis”).

            Content

            Author and article information

            Contributors
            Conference
            July 2019
            July 2019
            : 318-320
            Affiliations
            [0001]IBM

            Armii Krajowej str. 16, 30-50 Kraków,

            Poland
            Article
            10.14236/ewic/EVA2019.60
            240ce1b5-4ab9-4d58-ba62-d7d5a964410f
            © Shvets. Published by BCS Learning and Development Ltd. Proceedings of EVA London 2019, 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 2019
            EVA 2019
            London, UK
            8 - 11 July 2019
            Electronic Workshops in Computing (eWiC)
            Electronic Visualisation and the Arts
            History
            Product

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2019.60
            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
            Deep learning,CNN,Harmonic sequences clustering,Supervised learning,Convolutional LSTM

            REFERENCES

            1. 2015 Long-term recurrent convolutional networks for visual recognition and description Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) Boston 7-12 June 2015 2625 2634

            2. 2018 Visualization and Music Harmony: Design, Implementation, and Evaluation 22nd International Conference Information Visualisation Fisciano 10-13 July 2018 498 503 IEEE

            3. 2018 Statistical Evolutionary Laws in Music Styles arXiv:1809.05832 [physics.soc-ph] 21 February 2019

            4. 2014 Investigation of the activity-based teaching method in e-learning musical harmony course Proceeding of Electronic Visualisation and the Arts (EVA Florence 2014) Florence 7-8 May 2014 107 112 Firenze University Press Florence

            5. 2015 Convolutional, long short-term memory, fully connected deep neural networks 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Brisbane 19-24 April 2015 4580 4584 IEEE

            6. 2015 Schemographe: Application for a New Representation Technique and Methodology of Analysis in Tonal Harmony Lecture Notes in Computer Science 9027 212 223

            7. 2015 Show and tell: A neural image caption generator Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) Boston 7-12 June 2015 3156 3164

            8. 2015 Convolutional LSTM network: A machine learning approach for precipitation nowcasting Advances in neural information processing systems 802 810

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