  Deep representation learning offers a powerful paradigm for mapping input
data onto an organized embedding space and is useful for many music information
retrieval tasks. Two central methods for representation learning include deep
metric learning and classification, both having the same goal of learning a
representation that can generalize well across tasks. Along with
generalization, the emerging concept of disentangled representations is also of
great interest, where multiple semantic concepts (e.g., genre, mood,
instrumentation) are learned jointly but remain separable in the learned
representation space. In this paper we present a single representation learning
framework that elucidates the relationship between metric learning,
classification, and disentanglement in a holistic manner. For this, we (1)
outline past work on the relationship between metric learning and
classification, (2) extend this relationship to multi-label data by exploring
three different learning approaches and their disentangled versions, and (3)
evaluate all models on four tasks (training time, similarity retrieval,
auto-tagging, and triplet prediction). We find that classification-based models
are generally advantageous for training time, similarity retrieval, and
auto-tagging, while deep metric learning exhibits better performance for
triplet-prediction. Finally, we show that our proposed approach yields
state-of-the-art results for music auto-tagging.
