Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis
Abstract
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.
Cite
@article{arxiv.2210.15356,
title = {Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis},
author = {Axel Marmoret and Jérémy E. Cohen and Frédéric Bimbot},
journal= {arXiv preprint arXiv:2210.15356},
year = {2023}
}
Comments
4 pages, 7 figures. Accepted for publication at WASPAA 2023. The associated toolbox is available at https://gitlab.inria.fr/amarmore/autosimilarity_segmentation/-/tree/WASPAA23