mlsanityData to 13 Oct 2026

Music Composition with Deep Learning: A Review

arXiv 2108.12290 ↗First posted 27 Aug 2021cs.SD cs.AI eess.AScited by 12

Not on the map. Why?

Abstract (arXiv)

Generating a complex work of art such as a musical composition requires exhibiting true creativity that depends on a variety of factors that are related to the hierarchy of musical language. Music generation have been faced with Algorithmic methods and recently, with Deep Learning models that are being used in other fields such as Computer Vision. In this paper we want to put into context the existing relationships between AI-based music composition models and human musical composition and creativity processes. We give an overview of the recent Deep Learning models for music composition and we compare these models to the music composition process from a theoretical point of view. We have tried to answer some of the most relevant open questions for this task by analyzing the ability of current Deep Learning models to generate music with creativity or the similarity between AI and human composition processes, among others.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 31 of its 71 references that are in the map. First posted on arXiv 27 Aug 2021.

Builds on

31 of 71 references.

References · 31

  1. 2017Attention Is All You Needcited by 60,106
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  3. 2019A Comprehensive Survey on Transfer Learningcited by 1,157
  4. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 376
  5. 2018Music Transformercited by 209
  6. 2021Symbolic Music Generation with Diffusion Modelscited by 152
  7. 2017MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompanimentcited by 151
  8. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 148
  9. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  10. 2018A Hierarchical Latent Vector Model for Learning Long-Term Structure in Musiccited by 131
  11. 2016DeepBach: a Steerable Model for Bach Chorales Generationcited by 90
  12. 2020A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directionscited by 56
19 more
  1. 2019LakhNES: Improving multi-instrumental music generation with cross-domain pre-trainingcited by 54
  2. 2020MMM : Exploring Conditional Multi-Track Music Generation with the Transformercited by 49
  3. 2019Counterpoint by Convolutioncited by 42
  4. 2020Learning Interpretable Representation for Controllable Polyphonic Music Generationcited by 40
  5. 2021MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAEcited by 40
  6. 2018MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfercited by 33
  7. 2018Symbolic Music Genre Transfer with CycleGANcited by 28
  8. 2020PIANOTREE VAE: Structured Representation Learning for Polyphonic Musiccited by 25
  9. 2018Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generationcited by 20
  10. 2020Automatic Melody Harmonization with Triad Chords: A Comparative Studycited by 19
  11. 2016Song From PI: A Musically Plausible Network for Pop Music Generationcited by 13
  12. 2019Improving Automatic Jazz Melody Generation by Transfer Learning Techniquescited by 8
  13. 2016Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraintscited by 7
  14. 2017Interactive Music Generation with Positional Constraints using Anticipation-RNNscited by 7
  15. 2019Improving Polyphonic Music Models with Feature-Rich Encodingcited by 6
  16. 2016A Unit Selection Methodology for Music Generation Using Deep Neural Networkscited by 5
  17. 2017Generating Nontrivial Melodies for Music as a Servicecited by 3
  18. 2017Learning to Fuse Music Genres with Generative Adversarial Dual Learningcited by 2
  19. 2019The Bach Doodle: Approachable music composition with machine learning at scalecited by 2

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 2; 2025: 3.

12 papers.

  1. 2025SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinementcited by 22
  2. 2023A Survey of AI Music Generation Tools and Modelscited by 9
  3. 2025The GigaMIDI Dataset with Features for Expressive Music Performance Detectioncited by 7
  4. 2022Subjective Evaluation of Deep Learning Models for Symbolic Music Compositioncited by 5
  5. 2023EmoGen: Eliminating Subjective Bias in Emotional Music Generationcited by 5
  6. 2025Vision-to-Music Generation: A Surveycited by 4
  7. 2022A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectivescited by 3
  8. 2023Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methodscited by 3
  9. 2022FIGARO: Generating Symbolic Music with Fine-Grained Artistic Controlcited by 1
  10. 2024VCoME: Verbal Video Composition with Multimodal Editing Effectscited by 1
  11. 2023AI and the creative realm: A short review of current and future applicationscited by 0
  12. 2024The Evolution and Future Perspectives of Artificial Intelligence Generated Contentcited by 0