[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A Style-Specific Music Composition Neural Network

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Automatic music composition could dramatically decrease music production costs, lower the threshold for the non-professionals to compose as well as improve the efficiency of music creation. In this paper, we proposed an intelligent music composition neutral network to automatically generate a specific style of music. The advantage of our model is the innovative structure: we obtained the music sequence through an actor’s long short term memory, then fixed the probability of sequence by a reward-based procedure which serves as feedback to improve the performance of music composition. The music theoretical rule is introduced to constrain the style of generated music. We also utilized a subjective validation in experiment to guarantee the superiority of our model compared with state-of-the-art works.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. van der Zwaag MD, Janssen JH, Westerink JHDM (2013) Directing physiology and mood through music: validation of an affective music player. IEEE Trans Affect Comput 4(1):57–68

    Article  Google Scholar 

  2. Li Z, Tang J, Mei T (2019) Deep collaborative embedding for social image understanding. IEEE Trans Pattern Anal Mach Intell 41(9):2070–2083

    Article  Google Scholar 

  3. Li Z, Tang J (2015) Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Trans Multimed 17(11):1989–1999

    Article  Google Scholar 

  4. Margounakis D, Politis D (2012) Science of networking: new trends in music production, distribution and management. In: Proceedings of international conference on information society, London, pp 486–491

  5. Fernández JD, Vico F (2013) AI methods in algorithmic composition: a comprehensive survey. J Artif Intell Res 48:513–582

    Article  MathSciNet  Google Scholar 

  6. http://magenta.tensorflow.org. Accessed 2016

  7. Zhu X, Yang J, Zhang C, Zhang S (2019) Efficient utilization of missing data in cost-sensitive learning. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2019.2956530

    Article  Google Scholar 

  8. Yan M, Chan CA, Gygax AF, Yan J, Campbell L, Nirmalathas A, Leckie C (2019) Modeling the total energy consumption of mobile network services and applications. Energies 12(1):184

    Article  Google Scholar 

  9. Li Z, Tang J (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343–5355

    Article  MathSciNet  Google Scholar 

  10. Zhu X, Zhu Y, Zheng W (2019) Spectral rotation for deep one-step clustering. Pattern Recognit. https://doi.org/10.1016/j.patcog.2019.107175

    Article  Google Scholar 

  11. Balaban M, Ebcioglu K, Laske O (1992) Understanding music with AI: perspectives on music cognition. In: AAAI Press, pp 186–205

  12. Ebcioglu K (1990) An expert system for harmonizing chorales in the style of J. S. Bach. J Log Program 8(1–2):145–185

    Article  MathSciNet  Google Scholar 

  13. Biles JA (1994) GenJam: a genetic algorithm for generating jazz solos. ICMA 94:131–137

    Google Scholar 

  14. Johnson D (2015) Composing music with recurrent neural networks. Blog. http://www.hexahedria.com/2015/08/03/composing-musicwith-recurrent-neural-networks. Accessed 2015

  15. Waite E (2016) Generating long-term structure in songs and stories. http://magenta.tensorflow.org/2016/07/15/lookback-rnn-attention-rnn. Accessed 2016

  16. Hadjeres G, Pachet F, Nielsen F (2017) Deepbach: a steerable model for bach chorales generation. In: Proceedings of ICML, pp 1362–1371

  17. Lattner S, Grachten M, Widmer G (2018) Imposing higher-level structure in polyphonic music generation using convolutional restricted Boltzmann machines and constraints. J Creat Music Syst. https://doi.org/10.5920/jcms.2018.01

    Article  Google Scholar 

  18. Akbari M, Liang J (2018) Semi-recurrent CNN-based VAE–GAN for sequential data generation. In: Proceedings of ICASSP, pp 2321–2325

  19. Roberts A, Engel J, Raffel C, Hawthorne C (2018) A hierarchical latent vector model for learning long-term structure in music. In: Proceedings of ICML

  20. Brunner G, Konrad A, Wang Y, Wattenhofer R (2018) MIDI-VAE: modeling dynamics and instrumentation of music with applications to style transfer. In: Proceedings of ISMIR

  21. Yang L-C, Chou S-Y, Yang Y-H (2017) MidiNet: a convolutional generative adversarial networks for symbolic-domain music generation. In: Proceedings of ISMIR

  22. Dong HW, Hsiao WY, Yang LC, Yang YH (2018) MuseGAN: symbolic-domain music generation and accompaniment with multitrack sequential generative adversarial networks. In: Proceedings of AAAI

  23. Yu L, Zhang W, Wang J, Yu Y (2017) SeqGAN: sequence generative adversarial nets with policy gradient. In: Proceedings of AAAI

  24. Brunner G, Wang Y, Wattenhofer R, Zhao S (2018) Symbolic music genre transfer with CycleGAN. In: Proceedings of ICTAI

  25. Zhu X, Zhang S, Hu R, He W, Lei C, Zhu P (2019) One-step multi-view spectral clustering. IEEE Trans Knowl Data Eng 31(10):2022–2034

    Article  Google Scholar 

  26. van den Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A (2016) WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499

  27. Manzelli R, Thakkar V, Siahkamari A et al (2018) Conditioning deep generative raw audio models for structured automatic music. In: Proceedings of ISMIR

  28. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of NIPS, pp 2672–2680

  29. Gross R, Gu Y, Li W, Gauci M (2017) Generalizing GANs: a turing perspective. In: Proceedings of NIPS, pp 6316–6326

  30. Grondman I, Busoniu L, Lopes GAD, Babuska R (2012) A survey of actor–critic reinforcement learning: standard and natural policy gradients. IEEE Trans Syst Man Cybern 42(6):1291–1307

    Article  Google Scholar 

  31. Li L, Li D, Song T, Xu X (2018) Actor–critic learning control based on regularized temporal-difference prediction with gradient correction. IEEE Trans Neural Netw Learn Syst 29(12):5899–5909

    Article  Google Scholar 

  32. LeCun Y et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551

    Article  Google Scholar 

  33. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  34. Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv:1410.2329

  35. Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Proceedings of INTERSPEECH, pp 338–342

  36. Fan Y, Qian Y, Xie F, Soong FK (2014) TTS synthesis with bidirectional LSTM based recurrent neural networks. In: Proceedings of INTERSPEECH, pp 1964–1968

  37. Malandrino D, Pirozzi D, Zaccagnino R (2018) Visualization and music harmony: design, implementation and evaluation. In: Proceedings of 22nd international conference information visualisation, pp 498–503

  38. Chiu S, Shan M (2006) Computer music composition based on discovered music patterns. In: Proceedings of IEEE international conference on systems, man and cybernetics, pp 4401–4406

  39. Chathuranga EADY, Ratnayake HUW, Premaratne IA (2017) An expert system to generate chords for melodies composed in eastern music format. In: Proceedings of international conference on computer, communications and electronics, pp 501–504

  40. Mnih V, Kavukcuoglu K, Silver D et al (2013) Playing Atari with deep reinforcement learning. In: Proceedings of NIPS, pp 201–220

  41. Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533

    Article  Google Scholar 

  42. Watkin CJCH (1989) Learning from delayed rewards. Robot Auton Syst 15(4):233–235

    Google Scholar 

  43. Van Hasselt H, Guez A, Silver D (2016) Deep reinforcement learning with double Q-learning. In: Proceedings of AAAI, pp 2094–2100

  44. Van Hasselt H (2010) Double Q-learning. In: Proceedings of AAAI, pp 2613–2621

  45. Quan W, Wang K, Yan D, Zhang X (2018) Distinguishing between natural and computer-generated images using convolutional neural networks. IEEE Trans Inf Forensics Secur 13(11):2772–2787

    Article  Google Scholar 

  46. Sun T, Wang Y, Yang J, Hu X (2017) Convolution neural networks with two pathways for image style recognition. IEEE Trans Image Process 26(9):4102–4113

    Article  MathSciNet  Google Scholar 

  47. Wright C (2013) Listening to music. Nelson Education, Scarborough

    Google Scholar 

  48. Dhanalakshmy DM, Menon HP, Vinaya V (2017) Musical notes to MIDI conversion. In: Proceedings of international conference on advances in computing, communications and informatics, pp 799–804

  49. Colmenares G, Escalante R, Sans JF, Surós R (2011) Computational modeling of reproducing-piano rolls. Comput Music J 35(1):58–75

    Article  Google Scholar 

  50. Jaques N, Gu S, Turner RE, Eck D (2017) Tuning recurrent neural networks with reinforcement learning. arXiv:1611.02796

  51. Malkauthekar MD (2013) Analysis of Euclidean distance and Manhattan Distance measure in face recognition. In: Proceedings of international conference on computational intelligence and information technology, pp 503–507

  52. Wang L, Zhang Y, Feng J (2005) On the Euclidean distance of images. IEEE Trans PAMI 27(8):1334–1339

    Article  Google Scholar 

  53. Greche L, Jazouli M, Es-Sbai N, Majda A, Zarghili A (2017) Comparison between Euclidean and Manhattan distance measure for facial expressions classification. In: Proceedings of international conference on wireless technologies, embedded and intelligent systems, pp 1–4

  54. Wiriyachaiporn P, Chanasit K, Suchato A, Punyabukkana P, Chuangsuwanich E (2018) Algorithmic music composition comparison. In: Proceedings of international joint conference on computer science and software engineering, pp 1–6

Download references

Acknowledgements

We acknowledge the help of Chao Zhang and Xing Wang of Chenda Music Co., Ltd, Beijing.

Author information

Author notes

  1. Cong Jin and Yun Tie have contributed equally to this work.

Authors and Affiliations

  1. School of Information and Communication Engineering, Communication University of China, Beijing, 100024, China

    Cong Jin

  2. School of Information and Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Yun Tie & Yong Bai

  3. School of Animation and Digital Arts, Communication University of China, Beijing, 100024, China

    Xin Lv

  4. Communication University of China, Beijing, 100024, China

    Shouxun Liu

Authors
  1. Cong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun Tie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shouxun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shouxun Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This document is the results of the research project funded by the National Natural Science Foundation of China (Grant Nos. 61631016, 61901421 and 11571325), National Key R&D Program of China (Grant No. 2018YFB1403903) and supported by the Fundamental Research Funds for the Central Universities (Grant Nos. 2019E002, CUC19ZD003 and CUC200B017).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, C., Tie, Y., Bai, Y. et al. A Style-Specific Music Composition Neural Network. Neural Process Lett 52, 1893–1912 (2020). https://doi.org/10.1007/s11063-020-10241-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-020-10241-8

Keywords

Search

Navigation