Automatic Melody Harmonization with Triad Chords: A Comparative Study
Abstract
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.
Cite
@article{arxiv.2001.02360,
title = {Automatic Melody Harmonization with Triad Chords: A Comparative Study},
author = {Yin-Cheng Yeh and Wen-Yi Hsiao and Satoru Fukayama and Tetsuro Kitahara and Benjamin Genchel and Hao-Min Liu and Hao-Wen Dong and Yian Chen and Terence Leong and Yi-Hsuan Yang},
journal= {arXiv preprint arXiv:2001.02360},
year = {2021}
}
Comments
20 pages, 6 figures, published in Journal of New Music Research (JNMR), Volume 50 Issue 1