Large-Scale Weakly-Supervised Content Embeddingsfor Music Recommendation and Tagging

We explore content-based representation learning strategies tailored for
large-scale, uncurated music collections that afford only weak supervision
through unstructured natural language metadata and co-listen statistics. At the
core is a hybrid training scheme that uses classification and metric learning
losses to incorporate both metadata-derived text labels and aggregate co-listen
supervisory signals into a single convolutional model. The resulting joint text
and audio content embedding defines a similarity metric and supports prediction
of semantic text labels using a vocabulary of unprecedented granularity, which
we refine using a novel word-sense disambiguation procedure. As input to simple
classifier architectures, our representation achieves state-of-the-art
performance on two music tagging benchmarks.

• Machine Intelligence