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arXiv · 2001.02360

Automatic Melody Harmonization with Triad Chords: A Comparative Study

Yin-Cheng Yeh·Wen-Yi Hsiao·Satoru Fukayama·Tetsuro Kitahara·Benjamin Genchel·Hao-Min Liu·Hao-Wen Dong·Yian Chen·Terence Leong·Yi-Hsuan Yang
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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.

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Yin-Cheng Yeh, Wen-Yi Hsiao, Satoru Fukayama, Tetsuro Kitahara, Benjamin Genchel, Hao-Min Liu, Hao-Wen Dong, Yian Chen, Terence Leong, Yi-Hsuan Yang. 2020-01-08. Automatic Melody Harmonization with Triad Chords: A Comparative Study. https://arxiv.org/abs/2001.02360

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