Doctor of Philosophy, The Ohio State University, 2016, Music
This dissertation describes the motivation, methodology, structure and content of a new symbolic corpus of rap vocal transcriptions known as the Musical Corpus of Flow (MCFlow). This corpus is intended to afford and inform research into the sonic organization of rapped vocals. An operational music theory of rap is presented, identifying the most artistically important features of rapped vocals and their most basic organizational structures. This theory informs and motivates the sampling and encoding scheme of MCFlow, which is described in detail. The content of the current MCFlow dataset is described as well: the current dataset includes transcriptions of 124 hip-hop songs by 47 artists, comprising 6,107 measures of music which contain 54,248 rapped words. Several preliminary descriptive analyses of the current dataset are presented as illustrations of MCFlow's usefulness for: (1) identifying normative structures in rap; (2) comparing the styles of different artists; (3) studying the historical evolution of rap artistry. Information regarding access to MCFlow data and tools for analyzing the data are presented and the MCFlow online Graphical User Interface---usable by any user with no special software requirements---is described.
Committee: David Huron (Advisor); Graeme Boone (Committee Member); Johanna Devaney (Committee Member)
Subjects: Computer Science; Music