mlsanityData to 7 Oct 2026

A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives

arXiv 2210.13944 ↗First posted 25 Oct 2022cs.AI cs.SD eess.AScited by 3

Not on the map. Why?

Abstract (arXiv)

Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artificial intelligence models that imitate such processes. Music generation with Artificial Intelligence is an emerging field that is gaining much attention in the recent years. In this paper, we describe how humans compose music and how new AI systems could imitate such process by comparing past and recent advances in the field with music composition techniques. To understand how AI models and algorithms generate music and the potential applications that might appear in the future, we explore, analyze and describe the agents that take part of the music generation process: the datasets, models, interfaces, the users and the generated music. We mention possible applications that might benefit from this field and we also propose new trends and future research directions that could be explored in the future.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 83 of its 178 references that are in the map. First posted on arXiv 25 Oct 2022.

Builds on

83 of 178 references.

References · 83

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  7. 2022Generative Adversarial Networkscited by 5,603
  8. 2017Neural Discrete Representation Learningcited by 4,650
  9. 2013Auto-Encoding Variational Bayescited by 3,980
  10. 2015Variational Inference with Normalizing Flowscited by 2,161
  11. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  12. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
38 more
  1. 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
  2. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  3. 2022A Generalist Agentcited by 802
  4. 2022AudioLM: a Language Modeling Approach to Audio Generationcited by 611
  5. 2020Jukebox: A Generative Model for Musiccited by 535
  6. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  7. 2022AudioGen: Textually Guided Audio Generationcited by 292
  8. 2018Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Datasetcited by 215
  9. 2018Music Transformercited by 207
  10. 2020DDSP: Differentiable Digital Signal Processingcited by 177
  11. 2022It's Raw! Audio Generation with State-Space Modelscited by 171
  12. 2021Symbolic Music Generation with Diffusion Modelscited by 152
  13. 2017MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompanimentcited by 151
  14. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 148
  15. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  16. 2018A Hierarchical Latent Vector Model for Learning Long-Term Structure in Musiccited by 131
  17. 2020Fast Transformers with Clustered Attentioncited by 123
  18. 2016SampleRNN: An Unconditional End-to-End Neural Audio Generation Modelcited by 110
  19. 2021Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphscited by 109
  20. 2016DeepBach: a Steerable Model for Bach Chorales Generationcited by 90
  21. 2021MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Trainingcited by 89
  22. 2019Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantitycited by 86
  23. 2020POP909: A Pop-song Dataset for Music Arrangement Generationcited by 83
  24. 2017MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generationcited by 72
  25. 2021RAVE: A variational autoencoder for fast and high-quality neural audio synthesiscited by 72
  26. 2021MT3: Multi-Task Multitrack Music Transcriptioncited by 70
  27. 2020PopMAG: Pop Music Accompaniment Generationcited by 68
  28. 2018This Time with Feeling: Learning Expressive Musical Performancecited by 67
  29. 2016madmom: a new Python Audio and Music Signal Processing Librarycited by 63
  30. 2022General-purpose, long-context autoregressive modeling with Perceiver ARcited by 60
  31. 2020A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directionscited by 56
  32. 2019LakhNES: Improving multi-instrumental music generation with cross-domain pre-trainingcited by 54
  33. 2014Variational Recurrent Auto-Encoderscited by 53
  34. 2020MMM : Exploring Conditional Multi-Track Music Generation with the Transformercited by 48
  35. 2022Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generationcited by 48
  36. 2020Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modellingcited by 45
  37. 2019Counterpoint by Convolutioncited by 42
  38. 2019MelNet: A Generative Model for Audio in the Frequency Domaincited by 41

50 most cited of 83 All 83 (JSON)

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024From Role-Play to Drama-Interaction: An LLM Solutioncited by 42
  2. 2024Foundation Models for Music: A Surveycited by 34
  3. 2025Towards Enhanced Immersion and Agency for LLM-based Interactive Dramacited by 9