We introduce the MuSe-Toolbox - a Python-based open-source toolkit for creating a variety of continuous and discrete emotion gold standards. In a single framework, we unify a wide range of fusion methods and propose the novel Rater Aligned Annotation Weighting (RAAW), which aligns the annotations in a translation-invariant way before weighting and fusing them based on the inter-rater agreements between the annotations. Furthermore, discrete categories tend to be easier for humans to interpret than continuous signals. With this in mind, the MuSe-Toolbox provides the functionality to run exhaustive searches for meaningful class clusters in the continuous gold standards. To our knowledge, this is the first toolkit that provides a wide selection of state-of-the-art emotional gold standard methods and their transformation to discrete classes. Experimental results indicate that MuSe-Toolbox can provide promising and novel class formations which can be better predicted than hard-coded classes boundaries with minimal human intervention. The implementation (1) is out-of-the-box available with all dependencies using a Docker container (2).
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