  A commonly-cited reason for the poor performance of automatic chord
estimation (ACE) systems within music information retrieval (MIR) is that
non-chord tones (i.e., notes outside the supporting harmony) contribute to
error during the labeling process. Despite the prevalence of machine learning
approaches in MIR, there are cases where alternative approaches provide a
simpler alternative while allowing for insights into musicological practices.
In this project, we present a statistical model for predicting chord tones
based on music theory rules. Our model is currently focused on predicting chord
tones in classical music, since composition in this style is highly
constrained, theoretically making the placement of chord tones highly
predictable. Indeed, music theorists have labeling systems for every variety of
non-chord tone, primarily classified by the note's metric position and
intervals of approach and departure. Using metric position, duration, and
melodic intervals as predictors, we build a statistical model for predicting
chord tones using the TAVERN dataset. While our probabilistic approach is
similar to other efforts in the domain of automatic harmonic analysis, our
focus is on melodic reduction rather than predicting harmony. However, we hope
to pursue applications for ACE in the future. Finally, we implement our melody
reduction model using an existing symbolic visualization tool, to assist with
melody reduction and non-chord tone identification for computational musicology
researchers and music theorists.
