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Amuse: Human-AI Collaborative Songwriting with Multimodal Inspirations

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Kim, Yewon, Lee, Sung-Ju, Donahue, Chris·2025·Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3706598.3713818
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Abstract

Songwriting is often driven by multimodal inspirations, such as imagery, narratives, or existing music, yet songwriters remain unsupported by current music AI systems in incorporating these multimodal inputs into their creative processes. We introduce Amuse, a songwriting assistant that transforms multimodal (image, text, or audio) inputs into chord progressions that can be seamlessly incorporated into songwriters' creative processes. A key feature of Amuse is its novel method for generating coherent chords that are relevant to music keywords in the absence of datasets with paired examples of multimodal inputs and chords. Specifically, we propose a method that leverages multimodal large language models (LLMs) to convert multimodal inputs into noisy chord suggestions and uses a unimodal chord model to filter the suggestions. A user study with songwriters shows that Amuse effectively supports transforming multimodal ideas into coherent musical suggestions, enhancing users' agency and creativity throughout the songwriting process.

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  • Access to generation does not remove the need for human judgement.

    “Access to generation does not remove the need for human judgement, since rendered outputs still have to be evaluated, revised and organised within a larger creative structure, particularly when fully rendered audio forms part of an iterative production process (Kim et al., 2025).”

    From AI-assisted literary-to-song adaptation: a practice-based study of literary interpretation in a text-to-audio workflow (2026) · cites this paper as Kim et al., 2025

    Section 3.2.3 (p. 4) says participants valued manual songwriting and the ability to choose where to use and edit AI suggestions, supporting the need for human judgement. But the paper discusses editing modular musical elements, not evaluating and revising fully rendered audio within an iterative production process; the Introduction (p. 1) says completed audio songs are less supportive of that kind of iteration. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Authors
Kim, Y., Lee, S.-J., & Donahue, C.
Affiliations
  • Korea Advanced Institute of Science and Technology (KR)
  • Carnegie Mellon University (US)
ORCID
Venue
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2025-04-25
Pages
1-28
DOI
10.1145/3706598.3713818
OpenAlex
W4409736191
Cited by
23
References
13
Citations by year
2026: 20 · 2025: 3
FWCI
17.38
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
National Research Foundation of Korea (RS-2024-00337007)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Music Technology and Sound Studies
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
  • Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
Keywords
human-AI collaboration, songwriting, multimodal data, music generation, AI-assisted composition

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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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