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A novel Xi’an drum music generation method based on Bi-LSTM deep reinforcement learning

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Abstract

Chinese Folk Drum music is an excellent traditional cultural resource, it has brilliant historical and cultural heritage and excellent traditional cultural connotation. However, the survey found that the social and cultural values, tourism economic values, and national self-confidence embodied in folk drum music, such as Xi'an drum music, are far from being released, and even its own inheritance and development are facing difficulties. The research focuses on the automatic generation of Xi'an drum music, with the aim of further inheriting, developing, and utilizing this exceptional traditional cultural resource. While Artificial Intelligence (AI) music generation has gained popularity in recent years, most platforms primarily focus on modern music rather than Chinese folk music. To address these issues and the unique challenges faced by Xi'an drum music, this paper proposes a Bi-LSTM network-based deep reinforcement learning model. The model incorporates the distinctive characteristics of ancient Chinese music, such as pitch, chord, and mode, and utilizes the Actor-Critic algorithm in reinforcement learning. During the simulation generation stage, an improved method of generating strategies through reward and punishment scores is introduced. Additionally, the model takes into account abstract concept constraints, such as chord progression and music theory rules, which are translated into computer language. By constructing a chord reward mechanism and a music principle reward mechanism, the model achieves harmony constraints and enables the systematic generation of drum music. Experimental results demonstrate that the proposed model, based on Bi-LSTM deep reinforcement learning, can generate Xi'an drum music with high quality and artistic aesthetics. This research contributes to the preservation, development, and utilization of Xi'an drum music, leveraging advancements in AI music generation technology.

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References

  1. Ping Z, Zhe C, Guiling Y (2020) Research on the protection and inheritance of Xi’an drum music intangible cultural heritage [J]. Cooperative Economy and Technology 16:20–22. https://doi.org/10.13665/j.cnki.hzjjykj.2020.16.007

    Article  Google Scholar 

  2. Chordia P, Sastry A, Şentürk S (2011) Predictive tabla modelling using variable-length markov andhidden markov models[J]. J New Music Res 40(2):105–118

    Article  Google Scholar 

  3. Pachet F, Roy P (2011) Markov constraints: steerable generation of markov sequences[J]. Constraints 16(2):148–172

    Article  MathSciNet  Google Scholar 

  4. Briot J (2020) From artificial neural networks to deep learning for music generation: history, concepts and trends[J]. Neural Comput Appl 33(1):39–65

    Article  MathSciNet  Google Scholar 

  5. Shopynskyi M, Golian N, Afanasieva I (2020) Long short term memory model appliance for generating music compositions. 2020 IEEE international conference on problems of infocommunications. Science and Technology, Kharkiv, Ukraine, pp 239–242. https://doi.org/10.1109/PICST51311.2020.94

  6. Dan M, Yue W (2021) Overview of intelligent music production based on generation countermeasure network [J]. Res Comput Appl 38(03):641–646. https://doi.org/10.19734/j.issn.1001-3695.2020.02.0030

    Article  Google Scholar 

  7. Eck D, Schmidhuber J (2008) A first look at music composition using LSTM recurrent neural networks. Idsia Usi Supsi Instituto Dalle Molle, Lugano

  8. Oord A, Dieleman S, Zen H et al (2016) WaveNet: a generative model for raw audio[J]. CoRR,2016,abs/1609.03499. https://doi.org/10.48550/arXiv.1609.03499

  9. Yang LC, Chou SY, Yang YH (2017) MidiNet: a convolutional generative adversarial network for symbolic-domain music generation using 1D and 2D conditions[J]. https://doi.org/10.48550/arXiv.1703.10847

  10. Dong HW, Hsiao WY, Yang LC et al (2018) MuseGAN: Symbolic-domain music generation and accompaniment with multi-track sequential generative adversarial networks[J]. https://doi.org/10.48550/arXiv.1709.06298

  11. Briot J-P, Hadjeres G, Pachet F-D (2020) Deep learning techniques for music generation, vol 1. Springer, Heidelberg

    Book  Google Scholar 

  12. Huang CZA, Vaswani A, Uszkoreit J et al (2018) Music transformer: generating music with long-term structure (2018) [J]. arXiv preprint arXiv:1809.04281

  13. Roberts A, Engel J, Raffel C et al (2018) A hierarchical latent vector model for learning long-term structure in music[C]. International Conference on Machine Learning, pp 4364–4373. Applications, 2020, 32(9): 4773–4784

  14. Shahriar S, Al Roken N (2022) How can generative adversarial networks impact computer generated art? Insights from poetry to melody conversion. Int J Inf Manag Data Insights 2(1):100066

    Google Scholar 

  15. Carnovalini F, Rodà A (2020) Computational creativity and music generation systems: an introduction to the state of the art. Front Artif Intell 3:14

    Article  Google Scholar 

  16. Moysis L et al (2023) Music deep learning: deep learning methods for music signal processing—a review of the state-of-the-art. IEEE Access 11:17031–17052. https://doi.org/10.1109/ACCESS.2023.3244620

    Article  Google Scholar 

  17. Casella P, Paiva A (2001) Magenta: an architecture for real time automatic composition of background music[C]. International Workshop on Intelligent Virtual Agents, Berlin, pp 224–232

  18. Ghedini F, Pachet F, Roy P (2016) Creating music and texts with flow machines. In: Corazza G, Agnoli S (eds) Multidisciplinary contributions to the science of creative thinking. Creativity in the Twenty First Century. Springer, Singapore. https://doi.org/10.1007/978-981-287-618-8_18

    Chapter  Google Scholar 

  19. Huang YS, Yang YH (2020) Pop music transformer: beat-based modeling and generation of expressive pop piano compositions[C]. Proceedings of the 28th ACM international conference on multimedia, pp 1180–1188

  20. Nan N, Guan X, Wang Y et al (2022) Common quantitative characteristics of music melodies—pursuing the constrained entropy maximization casually in composition[J]. Sci China Inf Sci 65(7):174201

    Article  Google Scholar 

  21. Liu J, Dong Y, Cheng Z et al (2022) Symphony generation with permutation invariant language model[J]. arXiv preprint arXiv:2205.05448

  22. Zhang N (2020) Learning adversarial transformer for symbolic music generation [J]. IEEE Trans Neural Netw Learn Syst 34(4):1754–1763. https://doi.org/10.1109/TNNLS.2020.2990746

    Article  MathSciNet  Google Scholar 

  23. Guan F, Yu C, Yang S. A GAN model with self-attention mechanism to generate multi-instruments symbolic music[C]. 2019 international joint conference on neural networks. https://doi.org/10.1109/IJCNN.2019.8852291

  24. Shahriar S (2022) GAN computers generate arts a survey on visual arts, music, and literary text generation using generative adversarial network. Displays 73:102237

    Article  Google Scholar 

  25. Luo J et al (2020) MG-VAE: deep Chinese folk songs generation with specific regional styles. Proceedings of the 7th Conference on Sound and Music Technology (CSMT) Revised Selected Papers. Springer Singapore

  26. Zhengyu C, Kunqi Y (2016) A review of MIDI music theory in the new era [J]. Art Rev 23:3. https://doi.org/10.3969/j.issn.1008-3359.2016.23.039

    Article  Google Scholar 

  27. Yuanyue T (2020) Research on folk song composition based on Markov model and neural network[D]. South China University of Technology. https://doi.org/10.27151/d.cnki.ghnlu.2020.002944

  28. Haodong Z (2022) Comparison of Characteristics between Jiangzhou Drum Music and Xi’an Drum Music [J]. Voice of the Yellow River 11:32–34. https://doi.org/10.19340/j.cnki.hhzs.2022.11.015

    Article  Google Scholar 

  29. Liyun W (2015) Research on the color grading function of Chinese national pentatonic “partial tone” in melody[J]. Northern Music 000(007):128–130

    Google Scholar 

  30. Tingge W (1987) Viewing the relationship between Traditional Music and Contemporary Music Creation from Chinese Piano Music [J]. Chinese Musicology 03:79–85

    Google Scholar 

  31. Iliadis LA, Sotiroudis SP, Kokkinidis K, Sarigiannidis P, Nikolaidis S, Goudos SK (2022) Music deep learning: a survey on deep learning methods for music processing. 2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), Bremen, Germany, pp 1–4. https://doi.org/10.1109/MOCAST54814.2022.9837541

  32. Sajad S, Dharshika S, Meleet M (2021) Music generation for novices using Recurrent Neural Network (RNN). 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India, pp 1–6. https://doi.org/10.1109/ICSES52305.2021.9633906

  33. Remesh A, K AP, Sinith MS (2022) Symbolic domain music generation system based on LSTM architecture. 2022 Second International Conference on Next Generation Intelligent Systems (ICNGIS), Kottayam, India, pp 1–4. https://doi.org/10.1109/ICNGIS54955.2022.10079872

  34. Zou Y, Zou P, Zhao Y et al (2021) MELONS: generating melody with long-term structure using transformers and structure graph[J]. https://doi.org/10.48550/arXiv.2110.05020

  35. Hochreiter S, Schmidhuber J (1997) Long short-term memory[J]. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  36. Rajadhyaksha M, Lakhani N, Mudassir MA, Bhavathankar P (2022) Music generation with bi-directional long short term memory neural networks. 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp 1–6

  37. Cho K, Van Merriënboer B, Gulcehre C et al (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint ar Xiv:1406.1078

  38. Tao W, Cong J, Xiaobing L, Yun T, Lin Q (2021) Multi track music generation countermeasure network based on transformer [J]. Comput Appl 41(12):3585–3589

    Google Scholar 

  39. Qiu Y (2019) Research on music generation based on generative adversarial network[D]. University of Electronic Science and Technology of China

  40. Yong B (2020) Research and implementation of music generation based on deep reinforcement learning[D]. Zhengzhou University. https://doi.org/10.27466/d.cnki.gzzdu.2020.000157

  41. Dua M, Yadav R, Mamgai D, Brodiya S (2020) An improved RNN-LSTM based novel approach for sheet music generation[J]. Procedia Comput Sci 171(C):465–474

    Article  Google Scholar 

  42. Yong B, Lin Q (2020) Yun music generation based on reinforcement learning actor- critic algorithm[J]. Comput Appl Softw 37(05):118–122+182

  43. Chengai S, Xinfeng Z, Gang T (2019) Music generation based on reward value RNN and A2C model [J]. Software 40(07):96–99

    Google Scholar 

  44. Haoyu G, Lifeng C, Niangrong G (2022) Li Jiahao 5G URLLC bearer network slice traffic scheduling optimization based on reinforcement learning [J]. Comput Knowl Technol 18(13):9–13. https://doi.org/10.14004/j.cnki.ckt.2022.0883

    Article  Google Scholar 

  45. Haibin L (2022) Application of reinforcement learning in single-track music generation[D]. University of Chinese Academy of Sciences (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences). https://doi.org/10.27822/d.cnki.gszxj.2022.000129

  46. Ji S, Luo J, Yang X (2020) A comprehensive survey on deep music generation: multi-level representations, algorithms, evaluations, and future directions[J]. https://doi.org/10.48550/arXiv.2011.06801

  47. Ting G (2021) Research and implementation of music melody generation algorithm based on deep learning[D]. Beijing University of Posts and Telecommunications. https://doi.org/10.26969/d.cnki.gbydu.2021.001784

  48. Dannenberg RB (2015) Music understanding and the future of music performance[J]

  49. Shuntaro O, Masayuki O, Shinichiro T (2019) Efficient partition of integer optimization problems with one-hot encoding[J]. Sci Rep 9(1):13036

  50. Zheng M (2021) A method of generating Chinese pop music melody[C]. Technical Committee on Control Theory, Chinese Association of Automation, Chinese Association of Automation, Systems Engineering Society of China. School of Information Science and Technology, University of Science and Technology of China. https://doi.org/10.26914/c.cnkihy.2021.028723

Download references

Acknowledgements

This work is partly supported by the key laboratory funds of the Ministry of Culture and Tourism under grant No 2022-13, the National Natural Science Foundation of China under Grant No. 62377034, 61977044, the Shaanxi Key Science and Technology Innovation Team Project under Grant No. 2022TD-26, the key project of teaching management reform in Shaanxi Normal University under Grant No. 22GX-JG05.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Service Technology for Folk Song, Ministry of Culture and Tourism, Xi’an, 710119, China

    Peng Li, Tian-mian Liang, Yu-mei Cao, Xiao-ming Wang & Xiao-jun Wu

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Peng Li, Tian-mian Liang, Yu-mei Cao, Xiao-ming Wang & Xiao-jun Wu

  3. Engineering Laboratory of Teaching Information Technology of Shaanxi Province, Xi’an, 710119, China

    Peng Li, Tian-mian Liang, Yu-mei Cao, Xiao-ming Wang & Xiao-jun Wu

  4. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710062, China

    Peng Li, Tian-mian Liang, Yu-mei Cao, Xiao-ming Wang & Xiao-jun Wu

  5. Xi’an Key Laboratory of Cultural Tourism Resources Development and Utilization, Xi’an, 710062, China

    Peng Li, Tian-mian Liang, Yu-mei Cao, Xiao-ming Wang & Xiao-jun Wu

  6. Chang’an District Cultural Center, Xi’an, 710199, China

    Lin-yi Lei

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Contributions

Li Peng: Preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision – including pre-or postpublication stages.

Liang Tian-mian: Ideas; formulation or evolution of overarching research goals and aims; Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms; testing of existing code components; Writing—Original Draft.

Cao Yu-mei: Verification, whether as a part of the activity or separate, of the overall replication/ reproducibility of results/experiments and other research outputs.

Wang Xiao-ming: Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team.

Wu Xiao-jun: Management and coordination responsibility for the research activity planning and execution.

Lei Lin-yi: Provision of study resources.

Corresponding author

Correspondence to Peng Li.

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Li, P., Liang, Tm., Cao, Ym. et al. A novel Xi’an drum music generation method based on Bi-LSTM deep reinforcement learning. Appl Intell 54, 80–94 (2024). https://doi.org/10.1007/s10489-023-05195-y

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