In this paper, we present MINERVA, the first benchmark dataset for the detection of musical instruments in non-photorealistic, unrestricted image collections from the realm of the visual arts.
This article investigates the research question: How has the distribution of music and talk on the Danish Broadcasting Corporation’s radio channel P3 developed 1989-2019 by comparing a qualitative case study with a new large-scale study.
This paper examines musical artificial intelligence (AI) algorithms that can not only learn from big data, but learn in ways that would be familiar to a musician or music theorist.
We propose a generalizable approach to studying the topological properties of music collaboration networks within and between genres that relies on data from the freely available Discogs database.