# A Survey on Evaluation Metrics for Music Generation

arXiv: https://arxiv.org/abs/2509.00051 · HTML: https://mlsanity.com/paper/2509.00051/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2025.

## Built on by (most cited first, 6 of 6)

- [MuQ-Eval: An Open-Source Per-Sample Quality Metric for AI Music Generation Evaluation](https://mlsanity.com/paper/2603.22677/) (2026)
- [Evaluating Music Context Preservation: A Multi-facet Framework for Music Editing Systems](https://mlsanity.com/paper/2512.14629/) (2025)
- [LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance](https://mlsanity.com/paper/2603.04293/) (2026)
- [APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music](https://mlsanity.com/paper/2605.03395/) (2026)
- [Exploring LLMs for South Asian Music Understanding and Generation](https://mlsanity.com/paper/2606.05522/) (2026)
- [Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding](https://mlsanity.com/paper/2606.08722/) (2026)

## Abstract (arXiv)

Despite significant advancements in music generation systems, the methodologies for evaluating generated music have not progressed as expected due to the complex nature of music, with aspects such as structure, coherence, creativity, and emotional expressiveness. In this paper, we shed light on this research gap, introducing a detailed taxonomy for evaluation metrics for both audio and symbolic music representations. We include a critical review identifying major limitations in current evaluation methodologies which includes poor correlation between objective metrics and human perception, cross-cultural bias, and lack of standardization that hinders cross-model comparisons. Addressing these gaps, we further propose future research directions towards building a comprehensive evaluation framework for music generation evaluation.