mlsanityData to 14 Oct 2026

A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music

arXiv 2308.13736 ↗First posted 26 Aug 2023cs.SD cs.AI cs.HCcited by 7

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Abstract (arXiv)

In recent years, AI-generated music has made significant progress, with several models performing well in multimodal and complex musical genres and scenes. While objective metrics can be used to evaluate generative music, they often lack interpretability for musical evaluation. Therefore, researchers often resort to subjective user studies to assess the quality of the generated works, which can be resource-intensive and less reproducible than objective metrics. This study aims to comprehensively evaluate the subjective, objective, and combined methodologies for assessing AI-generated music, highlighting the advantages and disadvantages of each approach. Ultimately, this study provides a valuable reference for unifying generative AI in the field of music evaluation.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 28 of its 48 references that are in the map. First posted on arXiv 26 Aug 2023.

Builds on

28 of 48 references.

References · 28

  1. 2022Generative Adversarial Networkscited by 5,627
  2. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  3. 2020Jukebox: A Generative Model for Musiccited by 542
  4. 2023MusicLM: Generating Music From Textcited by 448
  5. 2015A note on the evaluation of generative modelscited by 292
  6. 2017Neural Audio Synthesis of Musical Notes with WaveNet Autoencoderscited by 225
  7. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  8. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224
  9. 2017MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompanimentcited by 151
  10. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  11. 2018A Hierarchical Latent Vector Model for Learning Long-Term Structure in Musiccited by 131
  12. 2017MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generationcited by 72
16 more
  1. 2020A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directionscited by 56
  2. 2019LakhNES: Improving multi-instrumental music generation with cross-domain pre-trainingcited by 54
  3. 2018MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfercited by 33
  4. 2021Controllable deep melody generation via hierarchical music structure representationcited by 30
  5. 2018Symbolic Music Genre Transfer with CycleGANcited by 28
  6. 2021TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Methodcited by 20
  7. 2020Automatic Melody Harmonization with Triad Chords: A Comparative Studycited by 19
  8. 2017A Hierarchical Recurrent Neural Network for Symbolic Melody Generationcited by 14
  9. 2022A Review of Intelligent Music Generation Systemscited by 11
  10. 2017Chord Generation from Symbolic Melody Using BLSTM Networkscited by 8
  11. 2017Music generation with variational recurrent autoencoder supported by historycited by 7
  12. 2016A Unit Selection Methodology for Music Generation Using Deep Neural Networkscited by 5
  13. 2020Generating Music with a Self-Correcting Non-Chronological Autoregressive Modelcited by 5
  14. 2021Modeling the Compatibility of Stem Tracks to Generate Music Mashupscited by 5
  15. 2022Subjective Evaluation of Deep Learning Models for Symbolic Music Compositioncited by 5
  16. 2018BandNet: A Neural Network-based, Multi-Instrument Beatles-Style MIDI Music Composition Machinecited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 3.

7 papers.

  1. 2024Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audiocited by 10
  2. 2025Survey on the Evaluation of Generative Models in Musiccited by 10
  3. 2024Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global Southcited by 3
  4. 2025On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomycited by 3
  5. 2026Fusing Memory and Attention: A study on LSTM, Transformer and Hybrid Architectures for Symbolic Music Generationcited by 0
  6. 2026APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Musiccited by 0
  7. 2026Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generationcited by 0