Putting the ‘Tech’ in Techno: Detecting Genres and Trendsetters in Electronic Music By Dirichlet Processes

Abstract

This thesis provides a foundation of code and models to mathematically analyze the evolution of Electronic Music (EM) over time. Using chronologically ordered data from the Million Song Dataset, it utilizes a Dirichlet Process Gaussian Mixture Model to assign the songs to clusters based on pitch and timbre data and without any previous assumptions of the clusters beforehand. By examining the characteristic sounds of songs in each cluster, the following conclusions are reached: 1. Which artists and songs were most innovative for their time 2. Potential new ways in which the genealogy of and relations between EM genres can be imagined Finally, this thesis evaluates the strengths and weaknesses of the model used and suggests future work that can be done to improve upon it.