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arXiv:2210.15356 (cs)
[Submitted on 27 Oct 2022 (v1), last revised 26 Sep 2023 (this version, v3)]

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

Authors:Axel Marmoret (1,2), Jérémy E. Cohen (3), Frédéric Bimbot (1) ((1) Univ. Rennes 1, Inria, CNRS, IRISA, France, (2) IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France, (3) CREATIS, Univ Lyon, CNRS)
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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.
Comments: 4 pages, 7 figures. Accepted for publication at WASPAA 2023. The associated toolbox is available at this https URL
Subjects: Sound (cs.SD); Information Retrieval (cs.IR); Audio and Speech Processing (eess.AS)
ACM classes: H.5.5
Cite as: arXiv:2210.15356 [cs.SD]
  (or arXiv:2210.15356v3 [cs.SD] for this version)
  https://doi.org/10.48550/arXiv.2210.15356

Submission history

From: Axel Marmoret [view email]
[v1] Thu, 27 Oct 2022 12:11:01 UTC (1,040 KB)
[v2] Tue, 19 Sep 2023 12:04:25 UTC (557 KB)
[v3] Tue, 26 Sep 2023 11:02:17 UTC (557 KB)
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