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A review of intelligent music generation systems

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Abstract

With the introduction of ChatGPT, the public’s perception of AI-generated content has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field’s development prospects.

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Notes

  1. http://abcnotation.com/wiki/abc:standard.

  2. https://magenta.tensorflow.org/.

  3. https://www.francoispachet.fr/.

References

  1. Agres K, Forth J, Wiggins GA (2016) Evaluation of musical creativity and musical metacreation systems. Comput Entertain CIE 14(3):1–33 (Publisher: ACM New York, NY, USA)

    Article  Google Scholar 

  2. Avdeeff M (2019) Artificial intelligence and popular music: SKYGGE, flow machines, and the audio uncanny valley. In: Arts, volume 8, page 130. Multidisciplinary Digital Publishing Institute. Issue: 4

  3. Berthelot D, Schumm T, Metz L (2017) Began: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717

  4. Briot J-P, Hadjeres G, Pachet F-D (2020) Deep learning techniques for music generation. Springer International Publishing, Cham, Computational Synthesis and Creative Systems

    Book  Google Scholar 

  5. Brunner G, Konrad A, Wang Y, Wattenhofer R (2018) MIDI-VAE: Modeling dynamics and instrumentation of music with applications to style transfer. arXiv preprint arXiv:1809.07600

  6. Brunner G, Wang Y, Wattenhofer R, Wiesendanger J (2017) JamBot: music theory aware chord based generation of polyphonic music with LSTMs. In: 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI), pp 519–526, Boston, MA. IEEE

  7. Brunner G, Wang Y, Wattenhofer R, Zhao S (2018) Symbolic music genre transfer with CycleGAN. arXiv:1809.07575 [cs, eess, stat]

  8. Budzianowski P, Vuli I (2019) Hello, It’s GPT-2—How can i help you? Towards the use of pretrained language models for task-oriented dialogue systems

  9. 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  PubMed  PubMed Central  Google Scholar 

  10. Chen K, Zhang W, Dubnov S, Xia G, Li W (2019) The effect of explicit structure encoding of deep neural networks for symbolic music generation. In: 2019 International workshop on multilayer music representation and processing (MMRP), pp 77–84. IEEE

  11. Choi K, Hawthorne C, Simon I, Dinculescu M, Engel J (2020) Encoding musical style with transformer autoencoders. In: International conference on machine learning, pp 1899–1908. PMLR

  12. Chu H, Urtasun R, Fidler S (2016) Song From PI: a musically plausible network for pop music generation. arXiv:1611.03477 [cs]

  13. De Prisco R, Zaccagnino G, Zaccagnino R (2020) EvoComposer: an evolutionary algorithm for 4-voice music compositions. Evolution Comput 28(3):489–530 (Publisher: MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info)

  14. Dhariwal P, Jun H, Payne C, Kim JW, Radford A, Sutskever I. Jukebox: a generative model for music. arXiv preprint arXiv:2005.00341

  15. Donahue C, McAuley J, Puckette M (2019b) Adversarial audio synthesis. arXiv:1802.04208 [cs]

  16. Delgado M, Fajardo W, Molina-Solana M (2009) Inmamusys: Intelligent multiagent music system. Exp Syst Appl 36(3):4574–4580

    Article  Google Scholar 

  17. Dong H-W, Hsiao W-Y, Yang L-C, Yang Y-H (2018) Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Thirty-second AAAI conference on artificial intelligence

  18. Dong H-W, Yang Y-H (2018) Convolutional generative adversarial networks with binary neurons for polyphonic music generation. arXiv:1804.09399 [cs, eess, stat]

  19. Dong H-W, Yang Y-H (2019) Generating Music with GANs https://salu133445.github.io/ismir2019tutor ial/pdf/ismir2019-tutorial-slides.pdf. Accessed 11 Jan 2022

  20. Engel J, Resnick C, Roberts A, Dieleman S, Norouzi M, Eck D, Simonyan K (20170) Neural audio synthesis of musical notes with waveNet autoencoders. In: International Conference on Machine Learning (pp. 1068-1077). PMLR

  21. Engel J, Agrawal KK, Chen S, Gulrajani I, Donahue C, Roberts A (2019) Gansynth: Adversarial neural audio synthesis. arXiv preprint arXiv:1902.08710

  22. Farzaneh M, Toroghi RM. GGA-MG: Generative genetic algorithm for music generation. arXiv preprint arXiv:2004.04687

  23. Fux JJ, Edmunds J (1965) The study of counterpoint from Johann Joseph Fux’s Gradus ad parnassum. Number 277. WW. Norton & Company

  24. Gillick J, Roberts A, Engel J, Eck D, Bamman D (2019) Learning to groove with inverse sequence transformations. In: International conference on machine learning (pp. 2269–2279). PMLR

  25. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Adv Neural Inf Process Syst, 27

  26. Guan F, Yu C, Yang S (2019) A GAN model with self-attention mechanism to generate multi-instruments symbolic music. In: 2019 International joint conference on neural networks (IJCNN)

  27. Hadjeres G, Nielsen F (2017) Interactive music generation with positional constraints using anticipation-RNNs. arXiv preprint arXiv:1709.06404

  28. Hadjeres G, Pachet F, Nielsen F (2017) DeepBach: a steerable model for bach chorales generation. In: International conference on machine learning, pp 119–127.PMLR

  29. Han C, Murao K, Noguchi T, Kawata Y, Uchiyama F, Rundo L, Nakayama H, Satoh S (2019) Learning more with less: Conditional PGGAN-based data augmentation for brain metastases detection using highly-rough annotation on MR images. In: Proceedings of the 28th ACM International conference on information and knowledge management, pp 119–127

  30. Herremans D, Chew E (2019) MorpheuS: generating structured music with constrained patterns and tension. IEEE Trans Affect Comput 10(4):510–523

    Article  Google Scholar 

  31. Herremans D, Chuan C-H, Chew E (2017) A functional taxonomy of music generation systems. ACM Comput Surv 50(5):1–30

    Article  Google Scholar 

  32. Hu X, Lee JH (2012, October) A cross-cultural study of music mood perception between american and chinese listeners. In: ISMIR (pp. 535–540)

  33. Hu X, Yang Y-H (2017) The mood of Chinese Pop music: pepresentation and recognition. J Assoc Inf Sci Technol

  34. Huang A, Wu R (2016) Deep learning for music. arXiv preprint arXiv:1606.04930

  35. Huang C-F, Lian Y-S, Nien W-P, Chieng W-H (2016) Analyzing the perception of Chinese melodic imagery and its application to automated composition. Multimedia Tools Appl 75(13):7631–7654

    Article  Google Scholar 

  36. Huang C-ZA, Cooijmans T, Roberts A, Courville A, Eck D (2019) Counterpoint by convolution. arXiv preprint arXiv:1903.07227

  37. Huang C-Z A, Vaswani A, Uszkoreit J, Shazeer N, Simon I, Hawthorne C, Dai AM, Hoffman MD, Dinculescu M, Eck D (2018) Music transformer. arXiv preprint arXiv:1809.04281

  38. Huang S, Li Q, Anil C, Oore S, Grosse RB (2019) Timbretron: A wavenet (cyclegan (cqt (audio))) pipeline for musical timbre transfer. arXiv preprint arXiv:1811.09620

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

  40. Jeong J, Kim Y, Ahn CW (2017) A multi-objective evolutionary approach to automatic melody generation. Exp Syst Appl 90:50–61 (Publisher: Elsevier)

  41. Jhamtani H, Berg-Kirkpatrick T (2019) Modeling self-repetition in music generation using generative adversarial networks. In: Machine learning for music discovery workshop, ICML

  42. Jiang J (2019) Stylistic melody generation with conditional variational auto-encoder

  43. Jiang J, Xia GG, Carlton DB, Anderson CN, Miyakawa RH (2020) Transformer VAE: a hierarchical model for structure-aware and interpretable music representation learning. In: ICASSP 2020—2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 516–520. ISSN: 2379-190X

  44. Jie CHEN (2015) Comparative study between Chinese and western music aesthetics and culture

  45. Jin C, Tie Y, Bai Y, Lv X, Liu S (2020) A style-specific music composition neural network. Neural Process Lett 52(3):1893–1912

    Article  Google Scholar 

  46. Kaliakatsos-Papakostas M, Floros A, Vrahatis MN (2020) Artificial intelligence methods for music generation: a review and future perspectives. Nat Inspired Comput Swarm Intell, pp 217–245. Publisher: Elsevier

  47. Kaliakatsos-Papakostas MA, Floros A, Vrahatis MN (2016) Interactive music composition driven by feature evolution. SpringerPlus 5(1):1–38 (Publisher: Springer)

  48. Keerti G, Vaishnavi A, Mukherjee P, Vidya AS, Sreenithya GS, Nayab D (2020) Attentional networks for music generation. arXiv preprint arXiv:2002.03854

  49. Kumar H, Ravindran B (2019) Polyphonic Music composition with LSTM neural networks and reinforcement learning. arXiv preprint arXiv:1902.01973

  50. Leach J, Fitch J (1995) Nature, music, and algorithmic composition. Comput Music J 19(2):23–33 (Publisher: JSTOR)

  51. Liang X, Wu J, Cao J (2019) MIDI-Sandwich2: RNN-based Hierarchical Multi-modal Fusion Generation VAE networks for multi-track symbolic music generation. arXiv:1909.03522 [cs, eess]. arXiv: 1909.03522

  52. Lin P-C, Mettrick D, Hung PC, Iqbal F (2018) Towards a music visualization on robot (MVR) prototype. In: 2018 IEEE international conference on artificial intelligence and virtual reality (AIVR), pp 256–257. IEEE

  53. Liu H-M, Yang Y-H (2018) Lead sheet generation and arrangement by conditional generative adversarial network. arXiv:1807.11161 [cs, eess]

  54. Lopes HB, Martins FVC, Cardoso RT, dos Santos VF (2017) Combining rules and proportions: A multiobjective approach to algorithmic composition. In: 2017 IEEE congress on evolutionary computation (CEC), pp 2282–2289. IEEE

  55. Loughran R, O’Neill M (2020) Evolutionary music: applying evolutionary computation to the art of creating music. Genet Program Evol Mach 21(1):55–85 (Publisher: Springer)

  56. Lousseief E, Sturm BLT, Sturm BL (2019) MahlerNet: Unbounded Orchestral Music with Neural Networks. In: the Nordic sound and music computing conference 2019 and the interactive sonification workshop (pp. 57–63)

  57. Lu C-Y, Xue M-X, Chang C-C, Lee C-R, Su L (2019) Play as you like: timbre-enhanced multi-modal music style transfer. Proc AAAI Conf Artif Intell 33:1061–1068

    Google Scholar 

  58. Luo J, Yang X, Ji S, Li J (2019) MG-VAE: Deep Chinese folk songs generation with specific regional style. arXiv:1909.13287 [cs, eess]

  59. Makris D, Kaliakatsos-Papakostas M, Karydis I, Kermanidis KL (2019) Conditional neural sequence learners for generating drums’ rhythms. Neural Comput Appl 31(6):1793–1804

    Article  Google Scholar 

  60. Manzelli R, Thakkar V, Siahkamari A, Kulis B (2018) Conditioning deep generative raw audio models for structured automatic music. arXiv preprint arXiv:1806.09905

  61. Manzelli R, Thakkar V, Siahkamari A, Kulis B (2018) An end to end model for automatic music generation: Combining deep raw and symbolic audio networks. In: Proceedings of the musical metacreation workshop at 9th international conference on computational creativity, Salamanca, Spain

  62. Medeot G, Cherla S, Kosta K, McVicar M, Abdallah S, Selvi M, Newton-Rex E, Webster K (2018) StructureNet: inducing structure in generated melodies. In: ISMIR, pp 725–731

  63. Mogren O (2016) C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv:1611.09904 [cs]

  64. Mura D, Barbarossa M, Dinuzzi G, Grioli G, Caiti A, Catalano MG (2018) A soft modular end effector for underwater manipulation.: a gentle, adaptable grasp for the ocean depths. IEEE Robot Autom Mag , 4:1–1

  65. Muñoz E, Cadenas JM, Ong YS, Acampora G (2014) Memetic music composition. IEEE Trans Evol Comput 20(1):1–15 (Publisher: IEEE)

  66. Olseng O, Gambäck B (2018) Co-evolving melodies and harmonization in evolutionary music composition. In: International conference on computational intelligence in music, sound, art and design, pp 239–255. Springer

  67. Oord Avd, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) WaveNet: a generative model for raw audio. arXiv:1609.03499 [cs]

  68. Oore S, Simon I, Dieleman S, Eck D, Simonyan K (2020) This time with feeling: learning expressive musical performance. Neural Comput Appl 32(4):955–967

    Article  Google Scholar 

  69. Payne C (2019) MuseNet.OpenAI Blog. https://openai.com/blog/musenet/. Accessed 11 Jan 2022

  70. Plut C, Pasquier P (2020) Generative music in video games: state of the art, challenges, and prospects. Entertain Comput 33:100337 (Publisher: Elsevier)

  71. Ramanto AS, No JG, Maulidevi DNU. Markov chain based procedural music generator with user chosen mood compatibility. In: Int J Asia Digital Art Des Assoc, 21(1):19–24

  72. Rivero D, Ramírez-Morales I, Fernandez-Blanco E, Ezquerra N, Pazos A (2020) Classical music prediction and composition by means of variational autoencoders. Appl Sci 10(9):3053

    Article  CAS  Google Scholar 

  73. Roberts A, Engel J, Raffel C, Hawthorne C, Eck D (2018) A hierarchical latent vector model for learning long-term structure in music. In: International conference on machine learning (pp 4364–4373). PMLR

  74. Scirea M, Togelius J, Eklund P, Risi S (2016) Metacompose: A compositional evolutionary music composer. In: International conference on computational intelligence in music, sound, art and design, pp 202–217. Springer

  75. Sturm BL, Ben-Tal O, Monaghan Ú, Collins N, Herremans D, Chew E, Hadjeres G, Deruty E, Pachet F (2019) Machine learning research that matters for music creation: a case study. J New Music Res 48(1):36–55 (Publisher: Taylor & Francis)

  76. Sturm BL, Santos JF, Ben-Tal O, Korshunova I (2016) Music transcription modelling and composition using deep learning. arXiv preprint arXiv:1604.08723

  77. Supper M (2001) A few remarks on algorithmic composition. Comput Music J 25(1):48–53

    Article  Google Scholar 

  78. Tapus A (2009) The role of the physical embodiment of a music therapist robot for individuals with cognitive impairments: longitudinal study. In: 2009 Virtual rehabilitation international conference, pp 203–203. IEEE

  79. Trieu N, Keller RM (2018) JazzGAN: Improvising with generative adversarial networks. In: MUME 2018: 6th international workshop on musical metacreation

  80. Valenti A, Carta A, Bacciu D (2020) Learning style-aware symbolic music representations by adversarial autoencoders. arXiv:2001.05494 [cs, stat]

  81. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser, Polosukhin I (2017) Attention is all you need. In: Adv Neural Inf Process Syst, pp 5998–6008

  82. Veblen K, Olsson B (2002) Community music: toward an international overview. The new handbook of research on music teaching and learning, pp 730–753

  83. Waite E, others (2016) Generating long-term structure in songs and stories. Web blog post. Magenta, 15(4)

  84. Wang B, Yang Y-H (2019) PerformanceNet: score-to-audio music generation with multi-band convolutional residual network. Proc AAAI Conf Artif Intell 33:1174–1181

    Google Scholar 

  85. Williams D, Hodge VJ, Gega L, Murphy D, Cowling PI, Drachen A (2019) AI and automatic music generation for mindfulness, p 11

  86. Wu C-W, Liu J-Y, Yang Y-H, Jang J-SR (2018) Singing style transfer using cycle-consistent boundary equilibrium generative adversarial networks. arXiv:1807.02254 [cs, eess]

  87. Yang L-C, Chou S-Y, Yan Y-H (2017) Midinet: A convolutional generative adversarial network for symbolic-domain music generation. arXiv preprint arXiv:1703.10847

  88. Yu Y, Srivastava A, Canales S (2021) Conditional LSTM-GAN for melody generation from lyrics. ACM Trans Multimedia Comput Commun Appl 17(1):1–20 arXiv:1908.05551

  89. Zhang N (2020) Learning adversarial transformer for symbolic music generation. IEEE, Publisher, IEEE Transactions on Neural Networks and Learning Systems

  90. Zhu H, Liu Q, Yuan NJ, Qin C, Li J, Zhang K, Zhou G, Wei F, Xu Y, Chen E (2018) XiaoIce Band: a melody and arrangement generation framework for pop music. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 2837–2846, London United Kingdom. ACM

  91. Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232

  92. Zipf GK (2016) Human behavior and the principle of least effort: an introduction to human ecology. Ravenio Books

  93. Hiller Jr LA, Isaacson LM (1957) Musical composition with a high speed digital computer. In: Audio engineering society convention 9. Audio Engineering Society

  94. Cope D (1991) Recombinant music: using the computer to explore musical style. Computer 24(7):22–28

    Article  Google Scholar 

  95. Miranda ER, Al Biles J (2007) Evolutionary computer music. Springer, Berlin

    Book  Google Scholar 

  96. Wei S, Xia G (2022) Learning long-term music representations via hierarchical contextual constraints. arXiv:2202.06180 [cs, eess]

  97. Guo R, Simpson I, Kiefer C, Magnusson T, Herremans D (2022) MusIAC: An extensible generative framework for music infilling applications with multi-level control. arXiv:2202.05528 [cs]

  98. Dong H-W, Chen K, Dubnov S, McAuley J, Berg-Kirkpatrick T (2023) Multitrack music transformer. arXiv:2207.06983 [cs, eess]

  99. Dubnov S, Chen K, Huang K. Deep musical information dynamics: novel framework for reduced neural-network music

  100. Yu B, Lu P, Wang R, Hu W, Tan X, Ye W, Zhang S, Qin T, Liu T-Y (2022) Museformer: transformer with fine- and coarse-grained attention for music generation. arXiv:2210.10349 [cs, eess]

  101. Zou Y, Zou P, Zhao Y, Zhang K, Zhang R, Wang X (2021) MELONS: generating melody with long-term structure using transformers and structure graph. arXiv:2110.05020 [cs, eess]

  102. Schäfer T, Sedlmeier P, Städtler C, Huron D (2013) The psychological functions of music listening. Front Psychol 4:511

    Article  PubMed  PubMed Central  Google Scholar 

  103. Ji S, Yang X, Luo J (2023) A survey on deep learning for symbolic music generation: representations, algorithms, evaluations, and challenges. ACM Comput Surv

  104. Chrome Music Lab, Chrome’s Song Maker. Accessed 22 Oct 2023, from https://musiclab.chromeexperiments.com/Song-Makerx

  105. Aiva Technologies SARL. (Copyright 2016-2023). AIVA. Accessed 22 Oct 2023, from https://www.aiva.ai/

  106. Choi K, Park J, Heo W, Jeon S, Park J (2021) Chord conditioned melody generation with transformer based decoders. IEEE Access 9:42071–42080. Conference Name: IEEE Access

  107. Lee S-g, Hwang U, Min S, Yoon S (2018) Polyphonic music generation with sequence generative adversarial networks. arXiv:1710.11418 [cs, eess]

  108. Mangal S, Modak R, Joshi P (2019) LSTM based music generation system. IARJSET 6(5):47–54 arXiv:1908.01080 [cs, eess, stat]

  109. Shin A, Crestel L, Kato H, Saito K, Ohnishi K, Yamaguchi M, Nakawaki M, Ushiku Y, Harada T (2017) Melody generation for pop music via word representation of musical properties. arXiv:1710.11549 [cs, eess]

  110. Wada Y, Nishikimi R, Nakamura E, Itoyama K, Yoshii K (2018) Sequential generation of singing F0 contours from musical note sequences based on WaveNet. In: 2018 Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC), pp 983–989. ISSN: 2640-0103

  111. Matsue J (2015) Focus: music in contemporary Japan. Routledge

  112. Mok AO (2014) East meets west: Learning-practices and attitudes towards music-making of popular musicians. Br J Music Educ 31(2):179–194

    Article  Google Scholar 

  113. Nooshin L, Widdess R (2006) Improvisation in Iranian and Indian music. J Indian Musicol Soc 36:104–119

    Google Scholar 

  114. Son JH (2015) Pagh-paan’s no-ul: Korean identity formation as synthesis of eastern and western music

  115. Repetto RC, Pretto N, Chaachoo A, Bozkurt B, Serra X (2018) An open corpus for the computational research of arab-andalusian music. In: Proceedings of the 5th international conference on digital libraries for musicology, pp 78–86

  116. Srinivasamurthy A, Gulati S, Repetto RC, Serra X (2021) Saraga: open datasets for research on indian art music. Emp Musicol Rev 16(1):85–98

    Google Scholar 

  117. Howard K (2016) Music as intangible cultural heritage: policy, ideology, and practice in the preservation of East Asian traditions. Routledge. Google-Books-ID: LYUWDAAAQBAJ

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

  119. Ji S, Luo J, Yang X (2020) A comprehensive survey on deep music generation: multi-level representations, algorithms, evaluations, and future directions. arXiv preprint arXiv:2011.06801

  120. Donahue C, Mao HH, Li YE, Cottrell GW, McAuley J (2019) LakhNES: Improving multi-instrumental music generation with cross-domain pre-training. arXiv:1907.04868 [cs, eess, stat]

  121. Simon I, Roberts A, Raffel C, Engel J, Hawthorne C, Eck D (2018) Learning a latent space of multitrack measures. arXiv:1806.00195 [cs, eess, stat]

  122. Thickstun J, Harchaoui Z, Kakade S (2016) Learning features of music from scratch. arXiv preprint arXiv:1611.09827

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Acknowledgements

Figure 4-11 are cited from relevant paper on music generation, and we extend our sincere appreciation to the authors for their contribution.

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  1. College of Electric and Information Engineering, Tongji University, Cao’an Street, Shanghai, 201804, China

    Lei Wang, Ziyi Zhao, Hanwei Liu, Junwei Pang & Qidi Wu

  2. Department of Music Engineering, Shanghai Conservatory of Music, Fenyang street, Shanghai, 200031, China

    Yi Qin

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Methodology: LW; Formal analysis and investigation: ZZ; Writing—original draft preparation: ZZ, HL; Data curation: JP; Writing—review and editing: YQ, SL, ZZ; Supervision: QW, LW; Funding acquisition: LW.

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Wang, L., Zhao, Z., Liu, H. et al. A review of intelligent music generation systems. Neural Comput & Applic 36, 6381–6401 (2024). https://doi.org/10.1007/s00521-024-09418-2

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