  Natural language processing methods have been applied in a variety of music
studies, drawing the connection between music and language. In this paper, we
expand those approaches by investigating \textit{chord embeddings}, which we
apply in two case studies to address two key questions: (1) what musical
information do chord embeddings capture?; and (2) how might musical
applications benefit from them? In our analysis, we show that they capture
similarities between chords that adhere to important relationships described in
music theory. In the first case study, we demonstrate that using chord
embeddings in a next chord prediction task yields predictions that more closely
match those by experienced musicians. In the second case study, we show the
potential benefits of using the representations in tasks related to musical
stylometrics.
