Yin-Cheng Yeh, Wen-Yi Hsiao, Satoru Fukayama, Tetsuro Kitahara, Benjamin Genchel, Hao-Min Liu, Hao-Wen Dong, Yian Chen, T. Leong, Yi-Hsuan Yang
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引用次数: 37
Abstract
The task of automatic melody harmonization aims to build a model that generates a chord sequence as the harmonic accompaniment of a given multiple-bar melody sequence. In this paper, we present a comparative study evaluating the performance of canonical approaches to this task, including template matching, hidden Markov model, genetic algorithm and deep learning. The evaluation is conducted on a dataset of 9226 melody/chord pairs, considering 48 different triad chords. We report the result of an objective evaluation using six different metrics and a subjective study with 202 participants, showing that a deep learning method performs the best.
期刊介绍：
The Journal of New Music Research (JNMR) publishes material which increases our understanding of music and musical processes by systematic, scientific and technological means. Research published in the journal is innovative, empirically grounded and often, but not exclusively, uses quantitative methods. Articles are both musically relevant and scientifically rigorous, giving full technical details. No bounds are placed on the music or musical behaviours at issue: popular music, music of diverse cultures and the canon of western classical music are all within the Journal’s scope. Articles deal with theory, analysis, composition, performance, uses of music, instruments and other music technologies. The Journal was founded in 1972 with the original title Interface to reflect its interdisciplinary nature, drawing on musicology (including music theory), computer science, psychology, acoustics, philosophy, and other disciplines.
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