Algorithmic and Historical Detection Patterns of Music Subcultures

Faculty: 
Carl DiSalvo
Students: 
David Jimison

The algorithmic detection of subcultural or niche taste trends is of growing importance in targeted advertising. This demonstration presents research using online music analysis tools from Spotify, MusicBrainz, and Rovi coupled with aggregated music listening behavior from Facebook users to detect individual tastes and emerging taste trends amongst social groups.

This research is presented alongside historical signifiers of music taste such as fashion, music collections, and subcultural knowledge.

The goal of the research is to display the growing importance of software-based taste detection algorithms in determining niche markets for online content providers, and some of the new methodologies available into such systems.

Lab: 
Faculty: 
Carl DiSalvo

The Digital Media graduate program at Georgia Tech provides students with a foundational, theoretical background in digital media and the opportunity to practice what is learned in the classroom through active participation in labs and research. The resources, facilities, and industry connections established and maintained by the program make our students some of the most sought-after graduates in the field today.