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

Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis