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

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

Anna Shvets

Conference

Publication date: July 2019

Publication date (Print): July 2019

Pages: 318-320

Affiliations

[0001]IBM

Armii Krajowej str. 16, 30-50 Kraków,

Poland

Article

DOI: 10.14236/ewic/EVA2019.60

SO-VID: 240ce1b5-4ab9-4d58-ba62-d7d5a964410f

License:

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/

Conference name: Proceedings of EVA London 2019

Conference acronym: EVA 2019

Conference number:

Conference location: London, UK

Conference date: 8 - 11 July 2019

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Electronic Visualisation and the Arts

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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/

REFERENCES

J. DonahueL Anne HendricksS. GuadarramaM. RohrbachS. VenugopalanK. SaenkoT. Darrell 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
D. MalandrinoD. PirozziR. Zaccagnino 2018 Visualization and Music Harmony: Design, Implementation, and Evaluation 22nd International Conference Information Visualisation Fisciano 10-13 July 2018 498 503 IEEE
E. NakamuraK. Kaneko 2018 Statistical Evolutionary Laws in Music Styles arXiv:1809.05832 [physics.soc-ph] 21 February 2019
P. PistoneA. Shvets 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
T. N. SainathO. VinyalsA. SeniorH. Sak 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
A. ShvetsM. Desainte-Catherine 2015 Schemographe: Application for a New Representation Technique and Methodology of Analysis in Tonal Harmony Lecture Notes in Computer Science 9027 212 223
O. VinyalsA. ToshevS. BengioD. Erhan 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
S. H. I. XingjianZ. ChenH. WangD. Y. YeungW. K. WongW. C. Woo 2015 Convolutional LSTM network: A machine learning approach for precipitation nowcasting Advances in neural information processing systems 802 810

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[1] J. DonahueL Anne HendricksS. GuadarramaM. RohrbachS. VenugopalanK. SaenkoT. Darrell 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] D. MalandrinoD. PirozziR. Zaccagnino 2018 Visualization and Music Harmony: Design, Implementation, and Evaluation 22nd International Conference Information Visualisation Fisciano 10-13 July 2018 498 503 IEEE

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

[4] P. PistoneA. Shvets 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] T. N. SainathO. VinyalsA. SeniorH. Sak 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] A. ShvetsM. Desainte-Catherine 2015 Schemographe: Application for a New Representation Technique and Methodology of Analysis in Tonal Harmony Lecture Notes in Computer Science 9027 212 223

[7] O. VinyalsA. ToshevS. BengioD. Erhan 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] S. H. I. XingjianZ. ChenH. WangD. Y. YeungW. K. WongW. C. Woo 2015 Convolutional LSTM network: A machine learning approach for precipitation nowcasting Advances in neural information processing systems 802 810

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