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

arXiv: https://arxiv.org/abs/2311.18604 · HTML: https://mlsanity.com/paper/2311.18604/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [Barwise Section Boundary Detection in Symbolic Music Using Convolutional Neural Networks](https://mlsanity.com/paper/2509.16566/) (2025)
- [Unsupervised Evaluation of Deep Audio Embeddings for Music Structure Analysis](https://mlsanity.com/paper/2603.27218/) (2026)

## Abstract (arXiv)

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.