  Music mood classification has been a challenging problem in comparison with
other music classification problems (e.g., genre, composer, or period). One
solution for addressing this challenge is to use an ensemble of machine
learning models. In this paper, we show that visualization techniques can
effectively convey the popular prediction as well as uncertainty at different
music sections along the temporal axis while enabling the analysis of
individual ML models in conjunction with their application to different musical
data. In addition to the traditional visual designs, such as stacked line
graph, ThemeRiver, and pixel-based visualization, we introduce a new variant of
ThemeRiver, called "dual-flux ThemeRiver", which allows viewers to observe and
measure the most popular prediction more easily than stacked line graph and
ThemeRiver. Together with pixel-based visualization, dual-flux ThemeRiver plots
can also assist in model-development workflows, in addition to annotating music
using ensemble model predictions.
