Abstract:
Audio embeddings of musical similarity are often used for music recommendations and autoplay discovery. These embeddings are typically learned using co-listen data to train a deep neural network, to provide consistent tripletloss distances. Instead of directly using these co-listen–based embeddings, we explore making recommendations based on a second, smaller embedding space of human-intelligible musical attributes. To do this, we use the co-listen–based audio embeddings as inputs to small attribute classifiers, trained on a small hand-labeled dataset. These classifiers map from the original embedding space to a new interpretable attribute coordinate system that provides a more useful distance measure for downstream applications. The attributes and attribute embeddings allow us to provide a search interface and more intelligible recommendations for music curators. We examine the relative performance of these two embedding spaces (the co-listen–audio embedding and the attribute embedding) for the mathematical separation of thematic playlists. We also report on the usefulness of recommendations from the attribute-embedding space to human curators for automatically extending thematic playlists.
