  The increasing accuracy of automatic chord estimation systems, the
availability of vast amounts of heterogeneous reference annotations, and
insights from annotator subjectivity research make chord label personalization
increasingly important. Nevertheless, automatic chord estimation systems are
historically exclusively trained and evaluated on a single reference
annotation. We introduce a first approach to automatic chord label
personalization by modeling subjectivity through deep learning of a harmonic
interval-based chord label representation. After integrating these
representations from multiple annotators, we can accurately personalize chord
labels for individual annotators from a single model and the annotators' chord
label vocabulary. Furthermore, we show that chord personalization using
multiple reference annotations outperforms using a single reference annotation.
