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The Ai music generation challenge 2021: Summary and results
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.ORCID-id: 0000-0003-2549-6367
2022 (engelsk)Inngår i: Proceedings of the 3rd Conference on AI Music Creativity, AIMC, 2022Konferansepaper, Publicerat paper (Fagfellevurdert)
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. 

sted, utgiver, år, opplag, sider
2022.
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-326350DOI: 10.5281/zenodo.7088406OAI: oai:DiVA.org:kth-326350DiVA, id: diva2:1753806
Konferanse
The 3rd Conference on AI Music Creativity (AIMC 2022), 13-15 September 2022, Virtual/Online
Forskningsfinansiär
EU, Horizon 2020, 864189
Merknad

QC 20230503

Tilgjengelig fra: 2023-04-28 Laget: 2023-04-28 Sist oppdatert: 2023-05-03bibliografisk kontrollert

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