Visualizing Ensemble Predictions of Music Mood
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Music mood classification has been a challenging problem in comparison with some other classification problems (e.g., genre, composer, or period). One solution for addressing this challenging is to use an of ensemble 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 introduced 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. Testing indicates that visualizing ensemble predictions is helpful both in model-development workflows and for annotating music using model predictions.
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