AI classifies songs from genres it has never heard before

Even casual music fans can distinguish songs by category without great difficulty, but that's not the case for computers. Most audio-based music classification and tagging systems use categorical supervised learning -- in other words, learning a function that maps songs to genres based on example pairs -- with a fixed set of labels that intrinsically can't handle unseen labels, such as newly added genres. That's why a team of scientists at Naver Corp, an internet content service company headquartered in South Korea, investigated a zero-shot alternative in a paper ("Zero-Shot Learning for Audio-based Music Classification and Tagging") published on the preprint server Arxiv.org. Their AI classification system learns how to recognize songs without any labeled training data by taking into account side information about musical instruments, words in descriptions about songs, and more. The researchers settled on two types of side information at the outset of the study: human-labeled attribute information and general word semantic information.