# A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music

arXiv: https://arxiv.org/abs/2308.13736 · HTML: https://mlsanity.com/paper/2308.13736/

## 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 (most cited first, 28 of 28)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment](https://mlsanity.com/paper/1709.06298/) (2017)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation](https://mlsanity.com/paper/1703.10847/) (2017)
- [A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions](https://mlsanity.com/paper/2011.06801/) (2020)
- [LakhNES: Improving multi-instrumental music generation with cross-domain pre-training](https://mlsanity.com/paper/1907.04868/) (2019)
- [MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer](https://mlsanity.com/paper/1809.07600/) (2018)
- [Controllable deep melody generation via hierarchical music structure representation](https://mlsanity.com/paper/2109.00663/) (2021)
- [Symbolic Music Genre Transfer with CycleGAN](https://mlsanity.com/paper/1809.07575/) (2018)
- [TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method](https://mlsanity.com/paper/2109.09617/) (2021)
- [Automatic Melody Harmonization with Triad Chords: A Comparative Study](https://mlsanity.com/paper/2001.02360/) (2020)
- [A Hierarchical Recurrent Neural Network for Symbolic Melody Generation](https://mlsanity.com/paper/1712.05274/) (2017)
- [A Review of Intelligent Music Generation Systems](https://mlsanity.com/paper/2211.09124/) (2022)
- [Chord Generation from Symbolic Melody Using BLSTM Networks](https://mlsanity.com/paper/1712.01011/) (2017)
- [Music generation with variational recurrent autoencoder supported by history](https://mlsanity.com/paper/1705.05458/) (2017)
- [A Unit Selection Methodology for Music Generation Using Deep Neural Networks](https://mlsanity.com/paper/1612.03789/) (2016)
- [Generating Music with a Self-Correcting Non-Chronological Autoregressive Model](https://mlsanity.com/paper/2008.08927/) (2020)
- [Modeling the Compatibility of Stem Tracks to Generate Music Mashups](https://mlsanity.com/paper/2103.14208/) (2021)
- [Subjective Evaluation of Deep Learning Models for Symbolic Music Composition](https://mlsanity.com/paper/2203.14641/) (2022)
- [BandNet: A Neural Network-based, Multi-Instrument Beatles-Style MIDI Music Composition Machine](https://mlsanity.com/paper/1812.07126/) (2018)

## Built on by (most cited first, 7 of 7)

- [Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio](https://mlsanity.com/paper/2407.14364/) (2024)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South](https://mlsanity.com/paper/2412.04100/) (2024)
- [On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2510.12201/) (2025)
- [Fusing Memory and Attention: A study on LSTM, Transformer and Hybrid Architectures for Symbolic Music Generation](https://mlsanity.com/paper/2603.21282/) (2026)
- [APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music](https://mlsanity.com/paper/2605.03395/) (2026)
- [Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation](https://mlsanity.com/paper/2608.03999/) (2026)

## 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.