Automatic Melody Harmonization with Triad Chords: A Comparative Study

Autor:	Yeh, Yin-Cheng, Hsiao, Wen-Yi, Fukayama, Satoru, Kitahara, Tetsuro, Genchel, Benjamin, Liu, Hao-Min, Dong, Hao-Wen, Chen, Yian, Leong, Terence, Yang, Yi-Hsuan
Rok vydání:	2020
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 20 pages, 6 figures, published in Journal of New Music Research (JNMR), Volume 50 Issue 1
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.02360 Zobrazit plný text záznamu View this record from Arxiv