mlsanityData to 7 Oct 2026

A Review of Intelligent Music Generation Systems

arXiv 2211.09124 ↗First posted 16 Nov 2022cs.SD cs.AI cs.MMcited by 11

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Abstract (arXiv)

With the introduction of ChatGPT, the public's perception of AI-generated content (AIGC) 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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 54 of its 133 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

54 of 133 references.

References · 54

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  3. 2020Jukebox: A Generative Model for Musiccited by 535
  4. 2017Neural Audio Synthesis of Musical Notes with WaveNet Autoencoderscited by 222
  5. 2018Adversarial Audio Synthesiscited by 222
  6. 2017MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompanimentcited by 151
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38 more
  1. 2017MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generationcited by 72
  2. 2018This Time with Feeling: Learning Expressive Musical Performancecited by 67
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  4. 2020A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directionscited by 56
  5. 2019LakhNES: Improving multi-instrumental music generation with cross-domain pre-trainingcited by 54
  6. 2022Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generationcited by 48
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  8. 2016Music transcription modelling and composition using deep learningcited by 36
  9. 2019Encoding Musical Style with Transformer Autoencoderscited by 35
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  12. 2018TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfercited by 32
  13. 2018A Functional Taxonomy of Music Generation Systemscited by 31
  14. 2019Conditional LSTM-GAN for Melody Generation from Lyricscited by 31
  15. 2018Symbolic Music Genre Transfer with CycleGANcited by 28
  16. 2022Multitrack Music Transformercited by 28
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  18. 2018Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generationcited by 20
  19. 2021MELONS: generating melody with long-term structure using transformers and structure graphcited by 18
  20. 2022MusIAC: An extensible generative framework for Music Infilling Applications with multi-level Controlcited by 17
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  22. 2018PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Networkcited by 11
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  24. 2018Conditioning Deep Generative Raw Audio Models for Structured Automatic Musiccited by 10
  25. 2018Learning a Latent Space of Multitrack Measurescited by 8
  26. 2019MIDI-Sandwich2: RNN-based Hierarchical Multi-modal Fusion Generation VAE networks for multi-track symbolic music generationcited by 8
  27. 2019MG-VAE: Deep Chinese Folk Songs Generation with Specific Regional Stylecited by 8
  28. 2017Interactive Music Generation with Positional Constraints using Anticipation-RNNscited by 7
  29. 2019Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Imagescited by 7
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  31. 2018Lead Sheet Generation and Arrangement by Conditional Generative Adversarial Networkcited by 6
  32. 2019LSTM Based Music Generation Systemcited by 5
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  35. 2019Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learningcited by 4
  36. 2020Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoderscited by 4
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  38. 2018Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networkscited by 2

50 most cited of 54 All 54 (JSON)

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Cited by year

Citing papers per year: 2023: 3; 2024: 4; 2025: 4.

11 papers.

  1. 2023The Ethical Implications of Generative Audio Models: A Systematic Literature Reviewcited by 18
  2. 2024Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Surveycited by 10
  3. 2025Survey on the Evaluation of Generative Models in Musiccited by 10
  4. 2023A Survey of AI Music Generation Tools and Modelscited by 9
  5. 2023A Comprehensive Survey for Evaluation Methodologies of AI-Generated Musiccited by 6
  6. 2024A Survey of Foundation Models for Music Understandingcited by 4
  7. 2025A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectivescited by 4
  8. 2025MusFlow: Multimodal Music Generation via Conditional Flow Matchingcited by 4
  9. 2024A Survey of Music Generation in the Context of Interactioncited by 2
  10. 2024Applications and Advances of Artificial Intelligence in Music Generation:A Reviewcited by 2
  11. 2025Training chord recognition models on artificially generated audiocited by 0