Automatic Melody Harmonization with Triad Chords: A Comparative Study

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 compar...

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Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Yin-Cheng, Yeh, Wen-Yi, Hsiao, Fukayama, Satoru, Kitahara, Tetsuro, Genchel, Benjamin, Hao-Min, Liu, Hao-Wen, Dong, Chen, Yian, Leong, Terence, Yang, Yi-Hsuan
Format: Article
Language:English
Subjects:
Chords (geometry)
Comparative studies
Genetic algorithms
Machine learning
Model matching
Template matching
Online Access:Get full text
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Summary: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.
ISSN:2331-8422