  Music Structure Analysis (MSA) consists of representing a song in sections
(such as ``chorus'', ``verse'', ``solo'' etc), and can be seen as the retrieval
of a simplified organization of the song. This work presents a new algorithm,
called Convolutive Block-Matching (CBM) algorithm, devoted to MSA. In
particular, the CBM algorithm is a dynamic programming algorithm, applying on
autosimilarity matrices, a standard tool in MSA. In this work, autosimilarity
matrices are computed from the feature representation of an audio signal, and
time is sampled on the barscale. We study three different similarity functions
for the computation of autosimilarity matrices. We report that the proposed
algorithm achieves a level of performance competitive to that of supervised
State-of-the-Art methods on 3 among 4 metrics, while being unsupervised.
