  Recent work has proposed the use of tensor decomposition to model repetitions
and to separate tracks in loop-based electronic music. The present work
investigates further on the ability of Nonnegative Tucker Decompositon (NTD) to
uncover musical patterns and structure in pop songs in their audio form.
Exploiting the fact that NTD tends to express the content of bars as linear
combinations of a few patterns, we illustrate the ability of the decomposition
to capture and single out repeated motifs in the corresponding compressed
space, which can be interpreted from a musical viewpoint. The resulting
features also turn out to be efficient for structural segmentation, leading to
experimental results on the RWC Pop data set which are potentially challenging
state-of-the-art approaches that rely on extensive example-based learning
schemes.
