  Despite the success of contrastive learning in Music Information Retrieval,
the inherent ambiguity of contrastive self-supervision presents a challenge.
Relying solely on augmentation chains and self-supervised positive sampling
strategies can lead to a pretraining objective that does not capture key
musical information for downstream tasks. We introduce semi-supervised
contrastive learning (SemiSupCon), a simple method for leveraging musically
informed labeled data (supervision signals) in the contrastive learning of
musical representations. Our approach introduces musically relevant supervision
signals into self-supervised contrastive learning by combining supervised and
self-supervised contrastive objectives in a simpler framework than previous
approaches. This framework improves downstream performance and robustness to
audio corruptions on a range of downstream MIR tasks with moderate amounts of
labeled data. Our approach enables shaping the learned similarity metric
through the choice of labeled data that (1) infuses the representations with
musical domain knowledge and (2) improves out-of-domain performance with
minimal general downstream performance loss. We show strong transfer learning
performance on musically related yet not trivially similar tasks - such as
pitch and key estimation. Additionally, our approach shows performance
improvement on automatic tagging over self-supervised approaches with only 5\%
of available labels included in pretraining.
