# MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI

arXiv: https://arxiv.org/abs/2608.30940 · HTML: https://mlsanity.com/paper/2608.30940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 22 of 22)

- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Music ControlNet: Multiple Time-varying Controls for Music Generation](https://mlsanity.com/paper/2311.07069/) (2023)
- [RAVE: A variational autoencoder for fast and high-quality neural audio synthesis](https://mlsanity.com/paper/2111.05011/) (2021)
- [Musika! Fast Infinite Waveform Music Generation](https://mlsanity.com/paper/2208.08706/) (2022)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Do Music Generation Models Encode Music Theory?](https://mlsanity.com/paper/2410.00872/) (2024)
- [Discovering and Steering Interpretable Concepts in Large Generative Music Models](https://mlsanity.com/paper/2505.18186/) (2025)
- [Combining audio control and style transfer using latent diffusion](https://mlsanity.com/paper/2408.00196/) (2024)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment](https://mlsanity.com/paper/2507.20880/) (2025)
- [MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI](https://mlsanity.com/paper/2507.03599/) (2025)
- [Opening Musical Creativity? Embedded Ideologies in Generative-AI Music Systems](https://mlsanity.com/paper/2508.08805/) (2025)
- [Music and Artificial Intelligence: Artistic Trends](https://mlsanity.com/paper/2508.11694/) (2025)
- [Workflow-Based Evaluation of Music Generation Systems](https://mlsanity.com/paper/2507.01022/) (2025)
- [AI-Assisted Music Production: A User Study on Text-to-Music Models](https://mlsanity.com/paper/2509.23364/) (2025)
- [Evaluating Disentangled Representations for Controllable Music Generation](https://mlsanity.com/paper/2602.10058/) (2026)

## Built on by (most cited first, 1 of 1)

- [SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds](https://mlsanity.com/paper/2609.04634/) (2026)

## Abstract (arXiv)

Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains underexplored. Prior work includes openness-focused evaluation frameworks, such as MusGO (Music-Generative Open AI), as well as qualitative studies of musicians' experiences with generative systems. However, these approaches do not support systematic comparison or early-stage discovery of models for creative use. Motivated by such limitations, we introduce MusGU+, a musician-centered evaluation framework organized around three dimensions: Adaptability, Usability, and Controllability. Together, these capture whether a model can be feasibly trained or fine-tuned on personal data, integrated into real-world music workflows, and controlled in musically meaningful ways. We evaluate 10 representative generative music systems and present an interactive discovery tool that enables musicians to explore and filter models according to these criteria. While MusGO remains valuable for promoting responsible research practices, MusGU+ supports informed selection and practical adoption of generative systems by musicians.