  Music Structure Analysis (MSA) consists in segmenting a music piece in
several distinct sections. We approach MSA within a compression framework,
under the hypothesis that the structure is more easily revealed by a simplified
representation of the original content of the song. More specifically, under
the hypothesis that MSA is correlated with similarities occurring at the bar
scale, this article introduces the use of linear and non-linear compression
schemes on barwise audio signals. Compressed representations capture the most
salient components of the different bars in the song and are then used to infer
the song structure using a dynamic programming algorithm. This work explores
both low-rank approximation models such as Principal Component Analysis or
Nonnegative Matrix Factorization and "piece-specific" Auto-Encoding Neural
Networks, with the objective to learn latent representations specific to a
given song. Such approaches do not rely on supervision nor annotations, which
are well-known to be tedious to collect and possibly ambiguous in MSA
description. In our experiments, several unsupervised compression schemes
achieve a level of performance comparable to that of state-of-the-art
supervised methods (for 3s tolerance) on the RWC-Pop dataset, showcasing the
importance of the barwise compression processing for MSA.
