  In music and speech, meaning is derived at multiple levels of context.
Affect, for example, can be inferred both by a short sound token and by sonic
patterns over a longer temporal window such as an entire recording. In this
letter, we focus on inferring meaning from this dichotomy of contexts. We show
how contextual representations of short sung vocal lines can be implicitly
learned from fundamental frequency ($F_0$) and thus be used as a meaningful
feature space for downstream Music Information Retrieval (MIR) tasks. We
propose three self-supervised deep learning paradigms which leverage pseudotask
learning of these two levels of context to produce latent representation
spaces. We evaluate the usefulness of these representations by embedding unseen
pitch contours into each space and conducting downstream classification tasks.
Our results show that contextual representation can enhance downstream
classification by as much as 15\% as compared to using traditional statistical
contour features.
