Clustering of Musical Pieces through Complex Networks: an Assessment over Guitar Solos

TL;DR

This paper explores a novel method of classifying guitar solos by modeling musical pieces as complex networks and clustering them based on network metrics, demonstrating its potential in multimedia applications.

Contribution

It introduces a new approach to music classification using complex network analysis specifically applied to guitar solos, with empirical validation.

Findings

Effective clustering of guitar solos using network metrics

Potential applications in music education and digital music generation

Demonstrates viability of complex networks for music categorization

Abstract

Musical pieces can be modeled as complex networks. This fosters innovative ways to categorize music, paving the way towards novel applications in multimedia domains, such as music didactics, multimedia entertainment and digital music generation. Clustering these networks through their main metrics allows grouping similar musical tracks. To show the viability of the approach, we provide results on a dataset of guitar solos.