# Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition

arXiv: https://arxiv.org/abs/2504.14071 · HTML: https://mlsanity.com/paper/2504.14071/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 62 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [This Time with Feeling: Learning Expressive Musical Performance](https://mlsanity.com/paper/1808.03715/) (2018)
- [LakhNES: Improving multi-instrumental music generation with cross-domain pre-training](https://mlsanity.com/paper/1907.04868/) (2019)
- [Deep Learning Techniques for Music Generation -- A Survey](https://mlsanity.com/paper/1709.01620/) (2017)
- [MMM : Exploring Conditional Multi-Track Music Generation with the Transformer](https://mlsanity.com/paper/2008.06048/) (2020)
- [Music transcription modelling and composition using deep learning](https://mlsanity.com/paper/1604.08723/) (2016)
- [Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators](https://mlsanity.com/paper/1901.06417/) (2019)

## Built on by (most cited first, 4 of 4)

- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home](https://mlsanity.com/paper/2606.07542/) (2026)
- [Adaptable Symbolic Music Infilling with MIDI-RWKV](https://mlsanity.com/paper/2506.13001/) (2025)
- [Supporting Creative Ownership through Deep Learning-Based Music Variation](https://mlsanity.com/paper/2509.25834/) (2025)

## Abstract (arXiv)

With the rise of artificial intelligence (AI), there has been increasing interest in human-AI co-creation in a variety of artistic domains including music as AI-driven systems are frequently able to generate human-competitive artifacts. Now, the implications of such systems for musical practice are being investigated. We report on a thorough evaluation of the user adoption of the Multi-Track Music Machine (MMM) as a co-creative AI tool for music composers. To do this, we integrate MMM into Cubase, a popular Digital Audio Workstation (DAW) by Steinberg, by producing a "1-parameter" plugin interface named MMM-Cubase (MMM-C), which enables human-AI co-composition. We contribute a methodological assemblage as a 3-part mixed method study measuring usability, user experience and technology acceptance of the system across two groups of expert-level composers: hobbyists and professionals. Results show positive usability and acceptance scores. Users report experiences of novelty, surprise and ease of use from using the system, and limitations on controllability and predictability of the interface when generating music. Findings indicate no significant difference between the two user groups.