STATISTICAL PARSING FOR HARMONIC ANALYSIS OF JAZZ CHORD SEQUENCES
Mark Granroth-Wilding, Mark J. Steedman · University of Michigan Library Repository · 2012
Analysing music resembles natural language parsing in requiring the derivation of structure from an unstructured and highly ambiguous sequence of elements, whether they are notes or words.Such analysis is fundamental to many music processing tasks, such as key identification and score transcription.The focus of the present paper is on harmonic analysis.We use the three-dimensional tonal harmonic space developed by [4,13,14] to define a theory of tonal harmonic progression, which plays a role analogous to semantics in language.Our parser applies techniques from natural language processing (NLP) to the problem of analysing harmonic progression.It uses a formal grammar of jazz chord sequences of a kind that is widely used for NLP, together with the statistically based modelling techniques standardly used in wide-coverage parsing, to map music onto underlying harmonic progressions in the tonal space.Using supervised learning over a small corpus of jazz chord sequences annotated with harmonic analyses, we show that grammar-based musical parsing using simple statistical parsing models is more accurate than a baseline Markovian model trained on the same corpus.