Clustering of Musical Pieces through Complex Networks: an Assessment over Guitar Solos
This work addresses music categorization for applications in multimedia domains, but it is incremental as it applies an existing method to new data.
The authors tackled the problem of categorizing music by modeling musical pieces as complex networks and clustering them based on network metrics, achieving results that demonstrate the approach's viability on a dataset of guitar solos.
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.