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Artificial Intelligence and Music: Open Questions of Copyright Law and Engineering Praxis
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-2549-6367
Joint Research Centre, European Commission.
Kingston University. (Department of Performing Arts)
Joint Research Centre, European Commission.ORCID iD: 0000-0002-2563-075X
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2019 (English)In: MDPI Arts, ISSN 2076-0752, Vol. 8, no 3, article id 115Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 8, no 3, article id 115
Keywords [en]
artificial intelligence; music; copyright; engineering; ethics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-257859DOI: 10.3390/arts8030115ISI: 000487985800024OAI: oai:DiVA.org:kth-257859DiVA, id: diva2:1348944
Note

QC 20190910

Available from: 2019-09-06 Created: 2019-09-06 Last updated: 2022-12-12Bibliographically approved

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fulltext(753 kB)620 downloads
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