Abstract
The task of automatic melody harmonization aims to build a model that generates a chord sequence as the harmonic accompaniment of a given multiple-bar melody sequence. In this paper, we present a comparative study evaluating the performance of canonical approaches to this task, including template matching, hidden Markov model, genetic algorithm and deep learning. The evaluation is conducted on a dataset of 9226 melody/chord pairs, considering 48 different triad chords. We report the result of an objective evaluation using six different metrics and a subjective study with 202 participants, showing that a deep learning method performs the best.
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