- freely available
Arts 2019, 8(3), 115; https://doi.org/10.3390/arts8030115
2. Copyright Law Perspective
3. Engineering Praxis Perspective
4. Implications of Copyright Law and Engineering Praxis for folkrnn
4.1. Legal Perspectives of folkrnn
4.2. Engineering Praxis Perspectives of folkrnn
Conflicts of Interest
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For instance, see the number of articles appearing like A. Moore’s “When AI Becomes an Everyday Technology”, Harvard Business Review, 7 June 2019, https://hbr.org/2019/06/when-ai-becomes-an-everyday-technology.
https://www.spotify.com is a music streaming service.
https://www.shazam.com is a music identification service.
https://www.amazon.com is an online retail service.
See for example these articles: https://www.motherjones.com/media/2019/03/what-will-happen-when-machines-write-songs-just-as-well-as-your-favorite-musician/; https://www.theverge.com/2019/4/17/18299563/ai-algorithm-music-law-copyright-human; https://www.theverge.com/2018/8/31/17777008/artificial-intelligence-taryn-southern-amper-music.
One source of data is https://thesession.org/.
See the Bottomless Tunebox YouTube channel: https://www.youtube.com/channel/UC7wzmG64y2IbTUeWji_qKhA/videos.
Art. 2 of the WIPO Performances and Phonograms Treaty (WPPT) defines phonogram as “the fixation of the sounds of a performance or of other sounds, or of a representation of sounds, other than in the form of a fixation incorporated in a cinematographic or other audiovisual work”.
S. 178 Copyright, Designs and Patents Act 1988.
Infopaq: C-5/08 Judgment of the Court (Fourth Chamber) of 16 July 2009; BSA: C-393/09, Judgment of the Court (Third Chamber) of 22 December 2010; Painer: C-145/10, Judgment of the Court (Third Chamber) of 1 December 2011; Dataco: Case 604/10, Judgment of the Court, (Third Chamber) of 1 March 2012.
See also presentations delivered at the European Copyright Society 2018 conference, “EU copyright, quo vadis? From the EU copyright package to the challenges of Artificial intelligence”: https://europeancopyrightsociety.org/ecs-conferences-2018-brussels. To our knowledge, there is no European case law yet dealing with AI-generated works. In China, a recent decision by Beijing Internet Court refused copyright protection for a report generated by AI. Under Chinese law, only works created by humans are eligible for protection. See Ming Chen, Beijing Internet Court denies copyright to works created solely by artificial intelligence, Journal of Intellectual Property Law & Practice, 2019, Vol. 14, No. 8.
In any case, humans would be needed, of course, for creating the technology. This does not mean that the generated music necessarily reflects the personality of those developers.
The European Commission has recently published a call for a study that will assess whether the current IPR framework is fit-for-purpose for AI-generated works/inventions: https://ec.europa.eu/digital-single-market/en/news/trends-and-developments-artificial-intelligence-challenges-intellectual-property-rights.
In Europe copyright lasts for 70 years from the death of the author. The duration of neighbouring rights is shorter (50 years) except for published phonograms. The use of works in the public domain is free.
Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC, OJ L 130, 17.5.2019, pp. 92–125.
Data mining is defined in Article 2 of the directive as “any automated analytical technique aimed at analysing text and data in digital form in order to generate information which includes but is not limited to patterns, trends and correlations”.
ibid., Article 3.
ibid., Article 4.
A rightholder can restrict use, e.g., explicitly using methods to block data mining is a way to opt out from the exception in case of material made publicly available online.
Judgment of 29 July 2019, Pelham, C-476/17, ECLI:EU:C:2019:624.
See the FAT/ML workshop: https://www.fatml.org.
“Intended” discrimination is commonly known in ML as disparate treatment, while “unintended” as disparate impact.
See Kate Crawford (2017), “Artificial intelligence with very real biases” https://www.wsj.com/articles/artificial-intelligencewith-very-real-biases-1508252717.
See, Fabien Gouyon (2018). “Overview and new challenges of music information research” https://www.slideshare.net/FabienGouyon/music-recommendation-2018-116102609.
Also see the tutorial, “Fairness, Accountability and Transparency in Music Information Research (FAT-MIR)” at the 2019 International Symposium on Music Information Retrieval, https://ismir2019.ewi.tudelft.nl/?q=node/41.
For example, see A. K. Raymond, “The Streaming Problem: How Spammers, Superstars, and Tech Giants Gamed the Music Industry”, Vulture, 5 July 2017, https://www.vulture.com/2017/07/streaming-music-cheat-codes.html.
The data is archived about once a week online: http://github.com/adactio/TheSession-data.
See footnote 21.
The first Gan Ainm on track 3, Gan Ainm, Gan Ainm, Gan Ainm.
See this discussion: https://thesession.org/discussions/39604.
See footnote 21.
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