Abstract:

This paper describe five different principled ways to embed songs into a Euclidean metric space. In particular, we learn embeddings so that the pairwise Euclidean distance between two songs reflects semantic dissimilarity. This allows distance-based analysis, such as for example straight-forward nearest-neighbor classification, to detect and potentially suggest similar songs within a collection. Each of the six approaches (baseline, whitening, LDA, NCA, LMNN and RCA) rotate and scale the raw feature space with a linear transform. We tune the parameters of these models using a song-classification task with content-based features.