kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
The Ai music generation challenge 2021: Summary and results
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-2549-6367
2022 (English)In: Proceedings of the 3rd Conference on AI Music Creativity, AIMC, 2022Conference paper, Published paper (Refereed)
Abstract [en]

We discuss the design and results of The Ai Music Generation Challenge 2021 and compare it to the challenge of the previous year. While the 2020 challenge was focused on the Irish double jig, the 2021 challenge was focused on a particular kind of Swedish traditional dance music, called slängpolska. Six systems participated in the 2021 challenge, each generating a number of tunes evaluated by five judges, all professional musicians and experts in the music style. In the first phase, the judges reject all tunes that are plagiarised, or that have incorrect meter or rhythm. In the second phase, they score the remaining tunes along four qualities: dancability, structure coherence, formal coherence, and playability. The judges know all the tunes are computer generated, but do not know what tunes come from what systems, or what kinds of machine learning and data are involved. In the third stage, the judges award prizes to the top tunes. This resulted in five tunes garnering first and second prizes, four of which come from one particular system. We perform a statistical analysis of the scores from all judges, which allows a quantitative comparison of all factors in the challenge. Finally, we look to the 2022 challenge. 

Place, publisher, year, edition, pages
2022.
National Category
Musicology
Identifiers
URN: urn:nbn:se:kth:diva-326350DOI: 10.5281/zenodo.7088406OAI: oai:DiVA.org:kth-326350DiVA, id: diva2:1753806
Conference
The 3rd Conference on AI Music Creativity (AIMC 2022), 13-15 September 2022, Virtual/Online
Funder
EU, Horizon 2020, 864189
Note

QC 20230503

Available from: 2023-04-28 Created: 2023-04-28 Last updated: 2023-05-03Bibliographically approved

Open Access in DiVA

fulltext(1383 kB)109 downloads
File information
File name FULLTEXT01.pdfFile size 1383 kBChecksum SHA-512
f8de9432156d2952d21bf56531077bf12066ba314b1fe73da65c0305cf7c2f8a1055f511ec9663875d85aaa6bac9c976c5aabe47588b19dcfe286f55d3f04871
Type fulltextMimetype application/pdf

Other links

Publisher's full textConference website

Authority records

Sturm, Bob

Search in DiVA

By author/editor
Sturm, Bob
By organisation
Speech, Music and Hearing, TMH
Musicology

Search outside of DiVA

GoogleGoogle Scholar
Total: 109 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 524 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf