# Adapting Frechet Audio Distance for Generative Music Evaluation

arXiv: https://arxiv.org/abs/2311.01616 · HTML: https://mlsanity.com/paper/2311.01616/

## Key facts

- Cited by 92 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2023.

## Built on by (most cited first, 30 of 92)

- [DITTO: Diffusion Inference-Time T-Optimization for Music Generation](https://mlsanity.com/paper/2401.12179/) (2024)
- [Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion](https://mlsanity.com/paper/2402.10009/) (2024)
- [ACE-Step: A Step Towards Music Generation Foundation Model](https://mlsanity.com/paper/2506.00045/) (2025)
- [Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis](https://mlsanity.com/paper/2603.06507/) (2026)
- [Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity](https://mlsanity.com/paper/2407.10387/) (2024)
- [DITTO-2: Distilled Diffusion Inference-Time T-Optimization for Music Generation](https://mlsanity.com/paper/2405.20289/) (2024)
- [KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation](https://mlsanity.com/paper/2502.15602/) (2025)
- [Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality](https://mlsanity.com/paper/2410.05203/) (2024)
- [Aligning Text-to-Music Evaluation with Human Preferences](https://mlsanity.com/paper/2503.16669/) (2025)
- [Presto! Distilling Steps and Layers for Accelerating Music Generation](https://mlsanity.com/paper/2410.05167/) (2024)
- [Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation](https://mlsanity.com/paper/2412.07948/) (2024)
- [FlowDec: A flow-based full-band general audio codec with high perceptual quality](https://mlsanity.com/paper/2503.01485/) (2025)
- [Benchmarking Music Generation Models and Metrics via Human Preference Studies](https://mlsanity.com/paper/2506.19085/) (2025)
- [COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations](https://mlsanity.com/paper/2404.16969/) (2024)
- [A Diffusion-Based Generative Equalizer for Music Restoration](https://mlsanity.com/paper/2403.18636/) (2024)
- [MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music Generation](https://mlsanity.com/paper/2407.15060/) (2024)
- [Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Models](https://mlsanity.com/paper/2408.07472/) (2024)
- [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)
- [Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfer](https://mlsanity.com/paper/2409.06096/) (2024)
- [Diversity-Rewarded CFG Distillation](https://mlsanity.com/paper/2410.06084/) (2024)
- [Naturalistic Music Decoding from EEG Data via Latent Diffusion Models](https://mlsanity.com/paper/2405.09062/) (2024)
- [MIDI-VALLE: Improving Expressive Piano Performance Synthesis Through Neural Codec Language Modelling](https://mlsanity.com/paper/2507.08530/) (2025)
- [Music Arena: Live Evaluation for Text-to-Music](https://mlsanity.com/paper/2507.20900/) (2025)
- [CMMD: Contrastive Multi-Modal Diffusion for Video-Audio Conditional Modeling](https://mlsanity.com/paper/2312.05412/) (2023)
- [AudioMorphix: Training-free audio editing with diffusion probabilistic models](https://mlsanity.com/paper/2505.16076/) (2025)
- [Quality-aware Masked Diffusion Transformer for Enhanced Music Generation](https://mlsanity.com/paper/2405.15863/) (2024)
- [Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation](https://mlsanity.com/paper/2410.17589/) (2024)
- [Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio](https://mlsanity.com/paper/2505.12863/) (2025)
- [Steering Autoregressive Music Generation with Recursive Feature Machines](https://mlsanity.com/paper/2510.19127/) (2025)

## Abstract (arXiv)

The growing popularity of generative music models underlines the need for perceptually relevant, objective music quality metrics. The Frechet Audio Distance (FAD) is commonly used for this purpose even though its correlation with perceptual quality is understudied. We show that FAD performance may be hampered by sample size bias, poor choice of audio embeddings, or the use of biased or low-quality reference sets. We propose reducing sample size bias by extrapolating scores towards an infinite sample size. Through comparisons with MusicCaps labels and a listening test we identify audio embeddings and music reference sets that yield FAD scores well-correlated with acoustic and musical quality. Our results suggest that per-song FAD can be useful to identify outlier samples and predict perceptual quality for a range of music sets and generative models. Finally, we release a toolkit that allows adapting FAD for generative music evaluation.