Emotion-flow guided music accompaniment generation
Abstract
The emotion of a music piece varies as it unrolls in time. We develop a system that takes a melody and an expected emotion flow as input and automatically generates an accompaniment. The accompaniment is composed of chord progression and accompaniment pattern. The former is generated from melody and valence data through dynamic programming, and the latter from arousal data. A mathematical model is developed to describe the relation between valence and chord progression. The performance of the system is evaluated subjectively. The cross-correlation coefficient between the expected arousals and the perceived ones is 0.84, and the cross-correlation coefficient between the expected valences and the perceived ones is 0.52. Both coefficients exceed 0.90 for musician subjects.
BibTeX
@inproceedings{icassp2016_emotionflowguide,
title = {Emotion-flow guided music accompaniment generation},
author = {Yi-Chan Wu and Homer H. Chen},
booktitle = {ICASSP 2016},
year = {2016}
}