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Literature survey of multi-track music generation model based on generative confrontation network in intelligent composition

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Abstract

The production of traditional music is too complicated, consuming a lot of financial and human resources. Therefore, this paper aims to use artificial intelligence (AI) for songwriting and to explore the development and application of the Generative Adversarial Network (GAN) in smart music. An improved GAN-based Multi-Track Music (MTM)-GAN is established. The model is validated with the generation of 5 different music tracks for bass, drums, guitar, piano, and strings. The verification results are compared with the music generated by the existing Multi-Track Sequential GAN (MuseGAN) index evaluation method. The results show that many music clips generated by the MTM-GAN model are smooth and have a certain artistic aesthetic effect. Through the comparison of the two convergence curves of MuseGAN and MTM-GAN, when the penalty term is increased, the MTM-GAN of Consistency Term (CT) converges faster, and the training process is more stable. The numerical space of the parameter distribution obtained by the MTM-GAN-based music segment test is significantly smaller than that of MuseGAN. The probability of MTM-GAN overfitting is small. 62.8% of music listeners cannot distinguish the generated melody from the real melody. Therefore, the proposed model has the advantages of a more stable, more realistic, and faster fitting speed in music generation, indicating that the music generation method is effective.

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References

  1. Al-Janabi S, Alkaim A, Al-Janabi E et al (2021) Intelligent forecaster of concentrations (PM2. 5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP). Neural Comput Appl 33(21):14199–14229

    Google Scholar 

  2. Al-Janabi S, Alkaim AF, Adel Z (2020) An innovative synthesis of deep learning techniques (DcapsNet & DCOM) for generation electrical renewable energy from wind energy. Soft Comput 24(14):10943–10962

    Google Scholar 

  3. Al-Janabi S, Alkaim AF (2020) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput 24(1):555–569

    Google Scholar 

  4. Al-Janabi S, Mohammad M, Al-Sultan A (2020) A new method for prediction of air pollution based on intelligent computation. Soft Comput 24(1):661–680

    Google Scholar 

  5. Al-Janabi S, Alwan E. (2017) Soft Mathematical System To Solve Black Box Problem Through Development the FARB Based On Hyperbolic And Polynomial Functions[C]//2017 10th International Conference On Developments In Esystems Engineering (DeSE). IEEE pp 37–42.

  6. Alkaim AF, Al_Janabi S. (2019) Multi Objectives Optimization To Gas Flaring Reduction From Oil Production[C]//International Conference On Big Data and Networks Technologies. Springer Cham pp 117–139.

  7. Al-Janabi S, Al-Shourbaji I, Shojafar M, et al. (2017) Mobile Cloud Computing: Challenges And Future Research Directions[C]//2017 10th International Conference On Developments In Esystems Engineering (DeSE). IEEE pp 62–67.

  8. SH Ali 2013Ali SH. (2013) Novel Approach For Generating The Key Of Stream Cipher System Using Random Forest Data Mining Algorithm[C]//2013 Sixth International Conference On Developments In Esystems Engineering. IEEE pp 259-269.

  9. Al-Janabi S, Rawat S, Patel A et al (2015) Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers. Int J Electr Power Energy Syst 67:324–335

    Google Scholar 

  10. Wen X (2021) Using deep learning approach and IoT architecture to build the intelligent music recommendation system. Soft Comput 25(4):3087–3096

    Google Scholar 

  11. Siphocly NNJ, El-Horbaty ESM (2019) Intelligent technique for automating the conversion between major and minor melodies. Future Comput Inform J 4(2):2

    Google Scholar 

  12. Paolizzo F, Johnson CG (2020) Creative autonomy in a simple interactive music system. J New Music Res 49(2):115–125

    Google Scholar 

  13. Rahate A, Walambe R, Ramanna S et al (2022) Multimodal co-learning: challenges, applications with datasets, recent advances and future directions. Inform Fus 81:203–239

    Google Scholar 

  14. Keerti G, Vaishnavi AN, Mukherjee P et al (2022) Attentional networks for music generation. Multimed Tools Appl 81(4):5179–5189

    Google Scholar 

  15. Rizvi SKJ, Azad MA, Fraz MM (2021) Spectrum of advancements and developments in multidisciplinary domains for generative adversarial networks (GANs). Arch Comput Method Eng 28(7):4503–4521

    Google Scholar 

  16. Hazra T, Anjaria K (2022) Applications of game theory in deep learning: a survey. Multimed Tools Appl 81(6):8963–8994

    Google Scholar 

  17. Supiarza H, Sarbeni I (2021) Teaching and learning music in digital era: creating keroncong music for gen z students through interpreting poetry. Harm J Arts Res Edu 21(1):123–139

    Google Scholar 

  18. Chang CY, Chen YP (2020) AntsOMG: a framework aiming to automate creativity and intelligent behavior with a showcase on cantus firmus composition and style development. Electronics 9(8):1212

    Google Scholar 

  19. Yu Y, Srivastava A, Canales S (2021) Conditional lstm-gan for melody generation from lyrics. ACM Trans Multimed Comput Commun Appl (TOMM) 17(1):1–20

    Google Scholar 

  20. Wu J, Hu C, Wang Y et al (2019) A hierarchical recurrent neural network for symbolic melody generation. IEEE Trans Cybern 50(6):2749–2757

    Google Scholar 

  21. Wang N, Xu H, Xu F et al (2021) The algorithmic composition for music copyright protection under deep learning and blockchain. Appl Soft Comput 112:107763

    Google Scholar 

  22. Siphocly NN, Salem ABM, El-Horabty ESM (2021) Applications of computational intelligence in computer music composition. Int J Intell Comput Inf Sci 21(1):59–67

    Google Scholar 

  23. Zheng Y (2020) The use of deep learning algorithm and digital media art in all-media intelligent electronic music system. PLoS ONE 15(10):e0240492

    Google Scholar 

  24. Jin C, Tie Y, Bai Y et al (2020) A style-specific music composition neural network. Neural Process L 52:1893–1912

    Google Scholar 

  25. Ali S H. (2012) Miner for OACCR: Case Of Medical Data Analysis In Knowledge Discovery[C]//2012 6th International Conference On Sciences Of Electronics, Technologies Of Information And Telecommunications (SETIT). IEEE pp 962–975.

  26. Al-Janabi S, Salman AH (2021) Sensitive integration of multilevel optimization model in human activity recognition for smartphone and smartwatch applications. B data min anal 4(2):124–138

    Google Scholar 

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

    Google Scholar 

  28. Tabuena AC (2020) Chord-interval, direct-familiarization, musical instrument digital interface, circle of fifths, and functions as basic piano accompaniment transposition techniques. Int J Res Publ 66(1):1–11

    Google Scholar 

  29. Paszke A, Gross S, Massa F et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8026–8037

    Google Scholar 

  30. Karras T, Aittala M, Laine S et al (2021) Alias-free generative adversarial networks. Adv Neural Inf Process Syst 34:852–863

    Google Scholar 

  31. Hadimlioglu IA, King SA (2018) Automated musical transitions through rule-based synthesis using musical properties. Entertain Comput 28:59–67

    Google Scholar 

  32. Zhang H, Xu T, Li H et al (2018) Stackgan++: realistic image synthesis with stacked generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 41(8):1947–1962

    Google Scholar 

  33. De Persis C, Grammatico S (2019) Distributed averaging integral nash equilibrium seeking on networks. Automatica 110:108–548

    MathSciNet  MATH  Google Scholar 

  34. Park SW, Huh JH, Kim JC (2020) BEGAN v3: avoiding mode collapse in GANs using variational inference. Electronics 9(4):6–88

    Google Scholar 

  35. Barzilay N, Shalev TB, Giryes R (2021) MISS GAN: a multi-illustrator style generative adversarial network for image to illustration translation. Pattern Recogn Lett 151:140–147

    Google Scholar 

  36. Wu X, Wang C, Lei Q (2020) Transformer-xl based music generation with multiple sequences of time-valued notes. arXiv Prepr arXiv 13:1–1

    Google Scholar 

  37. Song G, Wang Z, Han F et al (2020) Music auto-tagging using scattering transform and convolutional neural network with self-attention. Appl Soft Comput 96:106–702

    Google Scholar 

  38. Leikin A (2017) Not set in stone: mikhail pletnev’s rewrite of scriabin’s piano concerto. Perform Pract Rev 22(1):1–29

    Google Scholar 

  39. Zeng Z, Xiong Y, Guo W et al (2020) ERgene: python library for screening endogenous reference genes. Sci Rep 10(1):12–56

    Google Scholar 

  40. Noor TH, Zeadally S, Alfazi A et al (2018) Mobile cloud computing: challenges and future research directions. J Netw Comput Appl 115:70–85

    Google Scholar 

  41. Heo YJ, Kim BG, Roy PP (2021) Frontal face generation algorithm from multi-view images based on generative adversarial network. J Multimed Inf Syst 8(2):85–92

    Google Scholar 

  42. Ye X, Du J, Ye Y (2022) MasterplanGAN: facilitating the smart rendering of urban master plans via generative adversarial networks. Environ Plan B urban Anal City Sci 49(3):794–814

    Google Scholar 

  43. Mohammed DY, Al-Karawi KA, Duncan PJ et al (2019) Overlapped music segmentation using a new effective feature and random forests. Int J Artifi Intell 8(2):181–189

    Google Scholar 

  44. Liu Y (2021) Improved generative adversarial network and its application in image oil painting style transfer. Image Vis Comput 105:104087

    Google Scholar 

  45. Briot JP (2021) From artificial neural networks to deep learning for music generation: history, concepts and trends. Neural Comput Appl 33(1):39–65

    MathSciNet  Google Scholar 

  46. Grekow J, Dimitrova-Grekow T (2021) Monophonic music generation with a given emotion using conditional variational autoencoder. IEEE Access 9:129088–129101

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the help from the university colleagues.

Funding

This work was supported by the diversified reform of music and ear teaching in ordinary higher music majors (Grant no. 2017 Xiangcheng Institute Fa No.120.33).

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  1. College of Art, Hunan City University, Yiyang, 413000, China

    Weiming Liu

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  1. Weiming Liu
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Liu, W. Literature survey of multi-track music generation model based on generative confrontation network in intelligent composition. J Supercomput 79, 6560–6582 (2023). https://doi.org/10.1007/s11227-022-04914-5

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