  Music Structure Analysis (MSA) is a Music Information Retrieval task
consisting of representing a song in a simplified, organized manner by breaking
it down into sections typically corresponding to ``chorus'', ``verse'',
``solo'', etc. In this work, we extend an MSA algorithm called the Correlation
Block-Matching (CBM) algorithm introduced by (Marmoret et al., 2020, 2022b).
The CBM algorithm is a dynamic programming algorithm that segments
self-similarity matrices, which are a standard description used in MSA and in
numerous other applications. In this work, self-similarity matrices are
computed from the feature representation of an audio signal and time is sampled
at the bar-scale. This study examines three different standard similarity
functions for the computation of self-similarity matrices. Results show that,
in optimal conditions, the proposed algorithm achieves a level of performance
which is competitive with supervised state-of-the-art methods while only
requiring knowledge of bar positions. In addition, the algorithm is made
open-source and is highly customizable.
