Automatic Melody Harmonization with Triad Chords: A Comparative Study

• URL: http://arxiv.org/abs/2001.02360v3
• Date: Tue, 27 Apr 2021 10:03:07 GMT
• Title: Automatic Melody Harmonization with Triad Chords: A Comparative Study
• Authors: Yin-Cheng Yeh, Wen-Yi Hsiao, Satoru Fukayama, Tetsuro Kitahara, Benjamin Genchel, Hao-Min Liu, Hao-Wen Dong, Yian Chen, Terence Leong, and Yi-Hsuan Yang
• Abstract summary: We present a comparative study evaluating and comparing the performance of a set of canonical approaches to this task. 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.
• Score: 24.95868747256647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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