  Several prior works have proposed various methods for the task of automatic
melody harmonization, in which a model aims to generate a sequence of chords to
serve as the harmonic accompaniment of a given multiple-bar melody sequence. In
this paper, we present a comparative study evaluating and comparing the
performance of a set of canonical approaches to this task, including a template
matching based model, a hidden Markov based model, a genetic algorithm based
model, and two deep learning based models. The evaluation is conducted on a
dataset of 9,226 melody/chord pairs we newly collect for this study,
considering up to 48 triad chords, using a standardized training/test split. We
report the result of an objective evaluation using six different metrics and a
subjective study with 202 participants.
