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# Unveiling the Hierarchical Structure of Music by Multi-Resolution Community Detection

## Details

**Authors** Jacopo de Berardinis, Michalis Vamvakaris, Angelo Cangelosi, Eduardo Coutinho

**Year** 2020

**Publisher** Transactions of the International Society for Music Information Retrieval

**Kind of work** article

**Discipline** Computer Science & AI

**Secondary disciplines** Music Theory

**Applied** false

[Read it at the publisher](https://doi.org/10.5334/tismir.41) 
10.5334/tismir.41

## In authors' words

### Abstract

Human perception of musical structure is supposed to depend on the generation of hierarchies, which is inherently related to the actual organisation of sounds in music. Musical structures are indeed best retained by listeners when they form hierarchical patterns, with consequent implications on the appreciation of music and its performance. The automatic detection of musical structure in audio recordings is one of the most challenging problems in the field of music information retrieval, since even human experts tend to disagree on the structural decomposition of a piece of music. However, most of the current music segmentation algorithms in literature can only produce flat segmentations, meaning that they cannot segment music at different levels in order to reveal its hierarchical structure. We propose a novel methodology for the hierarchical analysis of music structure that is based on graph theory and multi-resolution community detection. This unsupervised method can perform both the tasks of boundary detection and structural grouping, without the need of particular constraints that would limit the resulting segmentation. To evaluate our approach, we designed an experiment that allowed us to compare its segmentation performance with that of the current state of the art algorithms for hierarchical segmentation. Our results indicate that the proposed methodology can achieve state of the art performance on a well-known benchmark dataset, thus providing a deeper analysis of musical structure.

### What they set out to do (purpose)

To develop and validate an unsupervised method that reveals music's nested, multi-level hierarchical structure from audio, rather than producing only a single flat segmentation.

### Who or what was studied (sample)

A well-known benchmark dataset for music structure analysis, used for computational evaluation rather than a human-subjects study.

### How they did it (methods)

A graph-theoretic, multi-resolution community-detection algorithm performing simultaneous boundary detection and structural grouping, benchmarked against existing hierarchical and flat segmentation algorithms.

### What they found (results)

An unsupervised graph-based multi-resolution community-detection algorithm recovered music's nested multi-level structure and achieved state-of-the-art performance against existing hierarchical segmentation algorithms on a standard benchmark, whereas most prior algorithms produced only flat, single-level segmentations.

## Commentary

### In short

Music is organized as a nested part-whole hierarchy across multiple levels, and that hierarchy can only be recovered by first identifying the boundaries between structural segments.

**Patterns it shows** D, S

**Added** 2026-09-22

**How to cite this** Jacopo de Berardinis, Michalis Vamvakaris, Angelo Cangelosi, Eduardo Coutinho (2020). Unveiling the Hierarchical Structure of Music by Multi-Resolution Community Detection. Transactions of the International Society for Music Information Retrieval.