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Artificial Intelligence and Music: Open Questions of Copyright Law and Engineering Praxis

Sturm, Bob L. T., Iglesias, Maria, Ben-Tal, Oded, Miron, Marius, Gómez, Emilia·2019·Arts
DOI 10.3390/arts8030115
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Abstract

The application of artificial intelligence (AI) to music stretches back many decades, and presents numerous unique opportunities for a variety of uses, such as the recommendation of recorded music from massive commercial archives, or the (semi-)automated creation of music. Due to unparalleled access to music data and effective learning algorithms running on high-powered computational hardware, AI is now producing surprising outcomes in a domain fully entrenched in human creativity—not to mention a revenue source around the globe. These developments call for a close inspection of what is occurring, and consideration of how it is changing and can change our relationship with music for better and for worse. This article looks at AI applied to music from two perspectives: copyright law and engineering praxis. It grounds its discussion in the development and use of a specific application of AI in music creation, which raises further and unanticipated questions. Most of the questions collected in this article are open as their answers are not yet clear at this time, but they are nonetheless important to consider as AI technologies develop and are applied more widely to music, not to mention other domains centred on human creativity.

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  • Model-derived regularities may be treated as authoritative, potentially subordinating practitioner knowledge about musically salient distinctions.

    “As generative outputs become reference points for style and genre, model-derived regularities may be treated as authoritative, potentially subordinating practitioner knowledge about musically salient distinctions such as groove, feel, swing, regional inflection, and microtiming (Danielsen et al. 2024;Sturm et al. 2019;Born et al. 2021).”

    From The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization (2026) · cites this paper as Sturm et al. 2019

    Section 3 (Engineering Praxis Perspective) warns that engineers may infer a music AI system’s usefulness from performance metrics without evaluating it in the context of users. Section 4.2 says sequence probabilities have limited relevance to musical value and describes bringing practitioners into evaluation. These passages support the concern about privileging model criteria, but do not say that generative outputs become authoritative reference points for style or genre, or that they subordinate practitioner knowledge of groove, feel, swing, regional inflection, or microtiming. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Representational imbalances in training data determine which musical practices are learned with enough stability to be reproduced.

    “as representational imbalances in training data determine which musical practices are learned with enough stability to be reproduced (Sturm et al. 2019).”

    From The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization (2026) · cites this paper as Sturm et al. 2019

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Citations

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Research works printed but not linked: 40

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Metadata

Authors
Sturm, B. L. T., Iglesias, M., Ben-Tal, O., Miron, M., & Gómez, E.
Affiliations
  • Division of Speech, Music and Hearing, School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH), 100 44 Stockholm, Sweden
  • Joint Research Centre, European Commission, 1050 Brussels, Belgium
  • Department of Performing Arts, Kingston University, Kingston KT2 7LB, UK
ORCID
Venue
Arts
Publisher
MDPI AG
Published
2019-09-06
Volume
8
Issue
3
Pages
115
ISSN
2076-0752
DOI
10.3390/arts8030115
Cited by
82
References
5
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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