This paper explores three distance measures and three statistical tests for the comparison of music expressed in abc format. We propose a methodology that allows for an analysis at the level of corpora (is the “style” represented in a corpus the same as that in the another corpus?) as well as at the level of item (is the “style” of an item that of the “style” represented in a corpus?). We estimate distributions of distances between item pairs within and between corpora, and test hypotheses that the distributions are identical. We empirically test the impact of distance measure and statistical test using a corpus of Irish traditional dance music and a collection of tunes generated by a machine learning model trained on the same. The proposed methodology has a variety of applications, from computational musicology, to evaluating machine generated music.

QC 20230911