Abstract
‘When in Rome’ brings together all human-made, computer-encoded, functional harmonic analyses of music. This amounts in total to over 2,000 analyses of 1,500 distinct works. The most obvious motivation is scale: gathering these datasets together leads to a corpus large and varied enough for tasks including machine learning for automatic analysis, composition, and classification, as well as at-scale anthology creation and more. Further benefits include bringing together a range of different composers and genres (previous datasets typically limit themselves to one context), and of analytical perspectives on those works. We offer this data in as ready-to-use and reproducible a state as possible at http://github.com/MarkGotham/When-in-Rome, with code and documentation for all tasks reported here, including corpus conversion routines and feature extraction.