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.

中文翻译：

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三和弦的自动旋律和声：一项比较研究

一些先前的工作已经提出了用于自动旋律协调任务的各种方法，其中模型旨在生成一系列和弦作为给定多小节旋律序列的和声伴奏。在本文中，我们提出了一项比较研究，评估和比较一组典型方法的性能，包括基于模板匹配的模型、基于隐马尔可夫的模型、基于遗传算法的模型和两个基于深度学习的模型。评估是在我们为这项研究新收集的 9,226 对旋律/和弦对的数据集上进行的，考虑了多达 48 个三和弦，使用标准化的训练/测试拆分。我们报告了使用六种不同指标的客观评估结果和 202 名参与者的主观研究。