The IsoVAT Corpus: Parameterization of Musical Features for Affective Composition

While there is a breadth of research in mapping Western musical features to perceived emotion within research in music and emotion, a critique of the field is that this breadth of methodologies lacks in inter-communication, which may reduce the generalizability of findings across the field. We consolidate previous research in this area to construct a parameterized composition guide that maps musical features to their associated emotional expression. We then use this guide to compose the “IsoVAT” dataset, a collection of symbolic MIDI clips in a variety of popular Western styles. This dataset contains a total of 90 clips of music, with 30 clips per affective dimension, organized into 10 sets of 3 clips. Each clip within a set is composed to express a low, medium, or high level of an affective dimension when compared to the other clips within the same set. We empirically evaluate the validity of our affective composition guide, to establish a ground-truth emotional expression in the dataset. Our validation reveals 19 sets where listener labels match the composed labels, 10 sets with listener labels that disagree with composed labels, and 1 clip that does not have clear agreement across the three study designs.