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Article Dans Une Revue Transactions of the International Society for Music Information Retrieval (TISMIR) Année : 2023

Barwise Music Structure Analysis with the Correlation Block-Matching Segmentation Algorithm

Résumé

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.
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Dates et versions

hal-04323556 , version 1 (05-12-2023)

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Axel Marmoret, Jérémy E Cohen, Frédéric Bimbot. Barwise Music Structure Analysis with the Correlation Block-Matching Segmentation Algorithm. Transactions of the International Society for Music Information Retrieval (TISMIR), 2023, 6 (1), pp.167-185. ⟨10.5334/tismir.167⟩. ⟨hal-04323556⟩
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