Quantifying music trends and facts using editorial metadata from the Discogs database

Comunicació presentada a: ISMIR 2017, celebrat a Suzhou, Xina, del 23 al 27 d'octubre de 2017While a vast amount of editorial metadata is being actively gathered and used by music collectors and enthusiasts, it is often neglected by music information retrieval and musicology researchers. In this paper we propose to explore Discogs, one of the largest databases of such data available in the public domain. Our main goal is to show how largescale analysis of its editorial metadata can raise questions and serve as a tool for musicological research on a number of example studies. The metadata that we use describes music releases, such as albums or EPs. It includes information about artists, tracks and their durations, genre and style, format (such as vinyl, CD, or digital files), year and country of each release. Using this data we study correlations between different genre and style labels, assess their specificity and analyze typical track durations. We estimate trends in prevalence of different genres, styles, and formats across different time periods. In our analysis of styles we use electronic music as an example. Our contribution also includes the tools we developed for our analysis and the generated datasets that can be re-used by MIR researchers and musicologists.This research has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 688382 (AudioCommons). We also thank Alastair Porter for proofreading

From Psychology to Phylogeny: Bridging Levels of Analysis in Cultural Evolution

Cultural evolution, or change in the socially learned behavior of a population over time, is a fascinating phenomenon that is widespread in humans and present in some non-human animals. In this dissertation, I present an array of cultural evolutionary studies that bridge pattern and process in a wide range of research models including music, extremism, and birdsong. The first chapter is an introduction to the field of cultural evolution, including a bibliometric analysis of its structure. The second and third chapters are studies on the cultural dynamics of music sampling traditions in hip-hop and electronic music communities and far-right extremism in the United States, using social network analysis and epidemiological modeling, respectively. The fourth and fifth chapters are studies on how cultural transmission biases influence population-level changes in music sampling traditions and house finch song, using a combination of agent-based modeling and machine learning. The sixth chapter is a technical report on computerized birdfeeders that were used to remotely collect data on the social network structure of a wild house finch population. Lastly, the seventh chapter applies a novel phylogenetic method based on dynamic community detection to reconstruct the cultural evolution of electronic music