# Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation

arXiv: https://arxiv.org/abs/2412.07948 · HTML: https://mlsanity.com/paper/2412.07948/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms](https://mlsanity.com/paper/1812.08466/) (2018)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [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)
- [Adapting Frechet Audio Distance for Generative Music Evaluation](https://mlsanity.com/paper/2311.01616/) (2023)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures](https://mlsanity.com/paper/2008.01307/) (2020)
- [Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer](https://mlsanity.com/paper/2111.04093/) (2021)
- [MusPy: A Toolkit for Symbolic Music Generation](https://mlsanity.com/paper/2008.01951/) (2020)
- [Music transcription modelling and composition using deep learning](https://mlsanity.com/paper/1604.08723/) (2016)
- [MidiCaps: A large-scale MIDI dataset with text captions](https://mlsanity.com/paper/2406.02255/) (2024)
- [Correlation of Fr'echet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant](https://mlsanity.com/paper/2403.17508/) (2024)
- [A database linking piano and orchestral MIDI scores with application to automatic projective orchestration](https://mlsanity.com/paper/1810.08611/) (2018)
- [Symbotunes: unified hub for symbolic music generative models](https://mlsanity.com/paper/2410.20515/) (2024)

## Built on by (most cited first, 15 of 15)

- [NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms](https://mlsanity.com/paper/2502.18008/) (2025)
- [Aligning Text-to-Music Evaluation with Human Preferences](https://mlsanity.com/paper/2503.16669/) (2025)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [Unveiling Audio Deepfake Origins: A Deep Metric learning And Conformer Network Approach With Ensemble Fusion](https://mlsanity.com/paper/2506.02085/) (2025)
- [BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps](https://mlsanity.com/paper/2604.19532/) (2026)
- [PhraseVAE and PhraseLDM: Latent Diffusion for Full-Song Multitrack Symbolic Music Generation](https://mlsanity.com/paper/2512.11348/) (2025)
- [How Far Can Pretrained LLMs Go in Symbolic Music? Controlled Comparisons of Supervised and Preference-based Adaptation](https://mlsanity.com/paper/2601.22764/) (2026)
- [BeatEdit: Symbolic Music Generation as Explicit Editing](https://mlsanity.com/paper/2607.11124/) (2026)
- [Conditional Diffusion as Latent Constraints for Controllable Symbolic Music Generation](https://mlsanity.com/paper/2511.07156/) (2025)
- [Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding](https://mlsanity.com/paper/2606.08722/) (2026)
- [Closing the Loop: PID Feedback Control for Interpretable Activation Steering in Symbolic Music Generation](https://mlsanity.com/paper/2606.18790/) (2026)
- [ChartGenEval: Corruption-Tested Multi-Dimensional Feedback for Rhythm-Game Chart Generation](https://mlsanity.com/paper/2607.12857/) (2026)
- [Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances](https://mlsanity.com/paper/2607.27909/) (2026)
- [Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation](https://mlsanity.com/paper/2608.03999/) (2026)
- [Harmonic Representations in Symbolic Music Transformers are Localized and Controllable](https://mlsanity.com/paper/2610.15957/) (2026)

## Abstract (arXiv)

In this paper we introduce the Frechet Music Distance (FMD), a novel evaluation metric for generative symbolic music models, inspired by the Frechet Inception Distance (FID) in computer vision and Frechet Audio Distance (FAD) in generative audio. FMD calculates the distance between distributions of reference and generated symbolic music embeddings, capturing abstract musical features. We validate FMD across several datasets and models. Results indicate that FMD effectively differentiates model quality, providing a domain-specific metric for evaluating symbolic music generation, and establishing a reproducible standard for future research in symbolic music modeling.